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Volumn , Issue , 2008, Pages 1-339

Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

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EID: 84889273432     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470377888     Document Type: Book
Times cited : (301)

References (420)
  • 1
    • 0003964436 scopus 로고    scopus 로고
    • Statistics and the Evaluation of Evidence for Forensic Scientists
    • 2nd ed. New York: Wiley.
    • C. G. G. Aitken and F. Taroni. Statistics and the Evaluation of Evidence for Forensic Scientists, 2nd ed. New York: Wiley. 2004.
    • (2004)
    • Aitken, C.G.G.1    Taroni, F.2
  • 2
    • 33847101445 scopus 로고    scopus 로고
    • A two-level model for evidence evaluation
    • Forthcoming
    • C. G. G. Aitken, G. Zadora, and D. Lucy. A two-level model for evidence evaluation. J. Forensic Sci. 52: 412-419. 2007. Forthcoming.
    • (2007) J. Forensic Sci. , vol.52 , pp. 412-419
    • Aitken, C.G.G.1    Zadora, G.2    Lucy, D.3
  • 3
    • 34547198475 scopus 로고    scopus 로고
    • The evaluation of evidence for exponentially distributed data
    • C. G. G. Aitken, Q. Shen, R. Jensen, and B. Hayes. The evaluation of evidence for exponentially distributed data. Comput. Stat. Data Anal. 12(12): 5682-5693. 2007.
    • (2007) Comput. Stat. Data Anal. , vol.12 , Issue.12 , pp. 5682-5693
    • Aitken, C.G.G.1    Shen, Q.2    Jensen, R.3    Hayes, B.4
  • 5
    • 9444264342 scopus 로고    scopus 로고
    • Rough Sets and Current Trends in Computing
    • Malvern, PA, October 14-16, 2002. Lecture Notes in Computer Science 2475. Berlin: Springer.
    • J. J. Alpigini, J. F. Peters, J. Skowronek, and N. Zhong, eds. Rough Sets and Current Trends in Computing. Proceedings. 3rd International Conference, Malvern, PA, October 14-16, 2002. Lecture Notes in Computer Science 2475. Berlin: Springer. 2002.
    • (2002) Proceedings. 3rd International Conference
    • Alpigini, J.J.1    Peters, J.F.2    Skowronek, J.3    Zhong, N.4
  • 6
    • 33750118115 scopus 로고    scopus 로고
    • Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach
    • K. K. Ang and C. Quek. Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach. IEEE Trans. Neural Net. 17(5): 1301-1315. 2006.
    • (2006) IEEE Trans. Neural Net. , vol.17 , Issue.5 , pp. 1301-1315
    • Ang, K.K.1    Quek, C.2
  • 7
    • 0028461417 scopus 로고
    • Automated learning of decision rules for text categorization
    • C. Apté, F. Damerau, and S. M. Weiss. Automated learning of decision rules for text categorization. ACM Trans. Inf. Sys. 12(3): 233-251. 1994.
    • (1994) ACM Trans. Inf. Sys. , vol.12 , Issue.3 , pp. 233-251
    • Apté, C.1    Damerau, F.2    Weiss, S.M.3
  • 8
    • 0142025113 scopus 로고    scopus 로고
    • An adaptive rough fuzzy single pass algorithm for clustering large data sets
    • S. Asharaf and M. N. Murty. An adaptive rough fuzzy single pass algorithm for clustering large data sets. Pattern Recog. 36(12): 3015-3018. 2004.
    • (2004) Pattern Recog. , vol.36 , Issue.12 , pp. 3015-3018
    • Asharaf, S.1    Murty, M.N.2
  • 9
    • 22844441843 scopus 로고    scopus 로고
    • Rough support vector clustering
    • S. Asharaf, S. K. Shevade, and N. M. Murty. Rough support vector clustering. Pattern Recog. 38(10): 1779-1783. 2005.
    • (2005) Pattern Recog. , vol.38 , Issue.10 , pp. 1779-1783
    • Asharaf, S.1    Shevade, S.K.2    Murty, N.M.3
  • 10
    • 3042692199 scopus 로고    scopus 로고
    • Combining information extraction with genetic algorithms for text mining
    • J. Atkinson-Abutridy, C. Mellish, and S. Aitken. Combining information extraction with genetic algorithms for text mining. IEEE Intelligent Systems 19(3): 22-30. 2004.
    • (2004) IEEE Intelligent Systems , vol.19 , Issue.3 , pp. 22-30
    • Atkinson-Abutridy, J.1    Mellish, C.2    Aitken, S.3
  • 12
    • 0031629657 scopus 로고    scopus 로고
    • An effective algorithm for discovering fuzzy rules in relational databases
    • Piscataway, NJ: IEEE Press
    • W. H. Au and K. C. C. Chan. An effective algorithm for discovering fuzzy rules in relational databases. In Proceedings of the 7th IEEE International Conference on Fuzzy Systems. Piscataway, NJ: IEEE Press, pp. 1314-1319. 1998.
    • (1998) Proceedings of the 7th IEEE International Conference on Fuzzy Systems. , pp. 1314-1319
    • Au, W.H.1    Chan, K.C.C.2
  • 13
    • 0003435075 scopus 로고    scopus 로고
    • Evolutionary Algorithms in Theory and Practice
    • Oxford: Oxford University Press.
    • T. Baeck. Evolutionary Algorithms in Theory and Practice. Oxford: Oxford University Press. 1996.
    • (1996)
    • Baeck, T.1
  • 14
    • 28244465505 scopus 로고    scopus 로고
    • Propositional satisfiability algorithm to find minimal reducts for data mining
    • A. A. Bakar, M. N. Sulaiman, M. Othman, and M. H. Selamat. Propositional satisfiability algorithm to find minimal reducts for data mining. Int. J. Comput. Math. 79(4): 379-389. 2002.
    • (2002) Int. J. Comput. Math. , vol.79 , Issue.4 , pp. 379-389
    • Bakar, A.A.1    Sulaiman, M.N.2    Othman, M.3    Selamat, M.H.4
  • 16
    • 0031185330 scopus 로고    scopus 로고
    • A mass assignment based ID3 algorithm for decision tree induction
    • J. F. Baldwin, J. Lawry, and T. P. Martin. A mass assignment based ID3 algorithm for decision tree induction. Int. J. Intell. Sys. 12(7): 523-552. 1997.
    • (1997) Int. J. Intell. Sys. , vol.12 , Issue.7 , pp. 523-552
    • Baldwin, J.F.1    Lawry, J.2    Martin, T.P.3
  • 17
    • 0012974464 scopus 로고    scopus 로고
    • An attempt to predict stock market data: a rough sets approach
    • Diploma thesis. Knowledge Systems Group, Department of Computer Systems and Telematics, Norwegian Institute of Technology, University of Trondheim.
    • J. K. Baltzersen. An attempt to predict stock market data: a rough sets approach. Diploma thesis. Knowledge Systems Group, Department of Computer Systems and Telematics, Norwegian Institute of Technology, University of Trondheim. 1996.
    • (1996)
    • Baltzersen, J.K.1
  • 19
    • 0037935625 scopus 로고    scopus 로고
    • A new method for avoiding abnormal conclusion for α-cut based rule interpolation
    • Seoul, Korea. Piscataway, NJ: IEEE Press
    • P. Baranyi, D. Tikk, Y. Yam, and L. T. Kóczy. A new method for avoiding abnormal conclusion for α-cut based rule interpolation. In Proceedings of FUZZ-IEEE'99 , Seoul, Korea. Piscataway, NJ: IEEE Press, pp. 383-388. 1999.
    • (1999) Proceedings of FUZZ-IEEE'99 , pp. 383-388
    • Baranyi, P.1    Tikk, D.2    Yam, Y.3    Kóczy, L.T.4
  • 20
    • 0003671169 scopus 로고
    • Fuzzy Rule-Based Modeling with Application to Geophysical, Biological and Engineering Systems
    • Roca Baton: CRC Press.
    • L. Bardossy and L. Duckstein. Fuzzy Rule-Based Modeling with Application to Geophysical, Biological and Engineering Systems. Roca Baton: CRC Press. 1995.
    • (1995)
    • Bardossy, L.1    Duckstein, L.2
  • 22
    • 0000255880 scopus 로고    scopus 로고
    • A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables
    • Heidelberg: Physica
    • J. Bazan. A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In Rough Sets in Knowledge Discovery. Heidelberg: Physica, pp. 321-365. 1998.
    • (1998) Rough Sets in Knowledge Discovery. , pp. 321-365
    • Bazan, J.1
  • 23
    • 0042656300 scopus 로고
    • Dynamic reducts as a tool for extracting laws from decision tables
    • In Z. W. Ras, M. Zemankova, eds., Lecture Notes in Artificial Intelligence 869. Berlin: Springer
    • J. Bazan, A. Skowron, and P. Synak. Dynamic reducts as a tool for extracting laws from decision tables. In Z. W. Ras, M. Zemankova, eds., Proceedings of 8th Symposium on Methodologies for Intelligent Systems. Lecture Notes in Artificial Intelligence 869. Berlin: Springer, pp. 346-355. 1994.
    • (1994) Proceedings of 8th Symposium on Methodologies for Intelligent Systems. , pp. 346-355
    • Bazan, J.1    Skowron, A.2    Synak, P.3
  • 24
    • 1542263675 scopus 로고
    • Market data analysis: A rough set approach. ICSResearch Reports 6/94
    • Warsaw University of Technology.
    • J. Bazan, A. Skowron, and P. Synak. Market data analysis: A rough set approach. ICSResearch Reports 6/94. Warsaw University of Technology. 1994.
    • (1994)
    • Bazan, J.1    Skowron, A.2    Synak, P.3
  • 25
    • 84889460608 scopus 로고    scopus 로고
    • Information measures for rough and fuzzy sets and application to uncertainty in relational databases
    • In [258]
    • T. Beaubouef, F. E. Petry, and G. Arora. Information measures for rough and fuzzy sets and application to uncertainty in relational databases. In [258] 1999.
    • (1999)
    • Beaubouef, T.1    Petry, F.E.2    Arora, G.3
  • 26
    • 0004020376 scopus 로고
    • Adaptive Control Processes: A Guided Tour
    • Princeton: Princeton University Press.
    • R Bellman. Adaptive Control Processes: A Guided Tour. Princeton: Princeton University Press. 1961.
    • (1961)
    • Bellman, R.1
  • 27
    • 84957795755 scopus 로고    scopus 로고
    • An investigation of β-reduct selection within the variable precision rough sets model
    • Lecture Notes in Computer Science, 2005, Springer-Verlag
    • M. J. Beynon. An investigation of β-reduct selection within the variable precision rough sets model. In Proceedings of 2nd International Conference on Rough Sets and Current Trends in Computing (RSCTC 2000), Lecture Notes in Computer Science, 2005, Springer-Verlag, pp 114-122. 2000.
    • (2000) Proceedings of 2nd International Conference on Rough Sets and Current Trends in Computing (RSCTC 2000) , pp. 114-122
    • Beynon, M.J.1
  • 28
    • 0035502119 scopus 로고    scopus 로고
    • Reducts within the variable precision rough sets model: A further investigation
    • M. J. Beynon. Reducts within the variable precision rough sets model: A further investigation. Eur. J. Oper. Res. 134(3): 592-605. 2001.
    • (2001) Eur. J. Oper. Res. , vol.134 , Issue.3 , pp. 592-605
    • Beynon, M.J.1
  • 29
    • 0242657729 scopus 로고    scopus 로고
    • Stability of continuous value discretisation: An application within rough set theory
    • M. J. Beynon. Stability of continuous value discretisation: An application within rough set theory. Int. J. Approx. Reason. 35: 29-53. 2004.
    • (2004) Int. J. Approx. Reason. , vol.35 , pp. 29-53
    • Beynon, M.J.1
  • 30
    • 0003543198 scopus 로고
    • Fuzzy mathematics in pattern classification
    • PhD thesis. Center for Applied Mathematics, Cornell University.
    • J. C. Bezdek, Fuzzy mathematics in pattern classification. PhD thesis. Center for Applied Mathematics, Cornell University. 1973.
    • (1973)
    • Bezdek, J.C.1
  • 31
    • 0004008854 scopus 로고
    • Pattern Recognition with Fuzzy Objective Function Algorithms
    • New York: Plenum Press.
    • J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press. 1981.
    • (1981)
    • Bezdek, J.C.1
  • 33
    • 17444379002 scopus 로고    scopus 로고
    • On fuzzy-rough sets approach to feature selection
    • R. B. Bhatt and M. Gopal. On fuzzy-rough sets approach to feature selection. Pattern Recog. Lett. 26(7): 965-975. 2005.
    • (2005) Pattern Recog. Lett. , vol.26 , Issue.7 , pp. 965-975
    • Bhatt, R.B.1    Gopal, M.2
  • 34
    • 19944424801 scopus 로고    scopus 로고
    • On the compact computational domain of fuzzy-rough sets
    • R. B. Bhatt and M. Gopal. On the compact computational domain of fuzzy-rough sets. Pattern Recog. Lett. 26(11): 1632-1640. 2005.
    • (2005) Pattern Recog. Lett. , vol.26 , Issue.11 , pp. 1632-1640
    • Bhatt, R.B.1    Gopal, M.2
  • 36
    • 0003487601 scopus 로고
    • Neural Networks for Pattern Recognition
    • Oxford: Oxford University Press.
    • C. M. Bishop. Neural Networks for Pattern Recognition. Oxford: Oxford University Press. 1995.
    • (1995)
    • Bishop, C.M.1
  • 38
    • 0003408496 scopus 로고    scopus 로고
    • UCI Repository of machine learning databases
    • Irvine: University of California., Available at
    • C. L. Blake and C. J. Merz. UCI Repository of machine learning databases. Irvine: University of California. 1998. Available at http://www.ics.uci.edu/~mlearn/.1
    • (1998) , pp. 1
    • Blake, C.L.1    Merz, C.J.2
  • 39
    • 0034501351 scopus 로고    scopus 로고
    • Upper and lower approximations of fuzzy sets
    • D. Boixader, J. Jacas, and J. Recasens. Upper and lower approximations of fuzzy sets. Int. J. Gen. Sys. 29(4): 555-568. 2000.
    • (2000) Int. J. Gen. Sys. , vol.29 , Issue.4 , pp. 555-568
    • Boixader, D.1    Jacas, J.2    Recasens, J.3
  • 40
    • 0003492070 scopus 로고    scopus 로고
    • Swarm Intelligence: From Natural to Artificial Systems
    • New York: Oxford University Press.
    • E. Bonabeau, M. Dorigo, and G. Theraulez. Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press. 1999.
    • (1999)
    • Bonabeau, E.1    Dorigo, M.2    Theraulez, G.3
  • 41
    • 0033714708 scopus 로고    scopus 로고
    • Interpolative reasoning based on graduality
    • San Antonio, TX. Piscataway, NJ: IEEE Press
    • B. Bouchon-Meunier, C. Marsala, and M. Rifqi. Interpolative reasoning based on graduality. In Proceedings of FUZZ-IEEE'2000 , San Antonio, TX. Piscataway, NJ: IEEE Press, pp. 483-487. 2000.
    • (2000) Proceedings of FUZZ-IEEE'2000 , pp. 483-487
    • Bouchon-Meunier, B.1    Marsala, C.2    Rifqi, M.3
  • 42
    • 1342330535 scopus 로고    scopus 로고
    • Is cross-validation valid for small-sample microarray classification?
    • U. M. Braga-Neto and E. R. Dougherty. Is cross-validation valid for small-sample microarray classification? Bioinformatics 20(3): 374-380. 2004.
    • (2004) Bioinformatics , vol.20 , Issue.3 , pp. 374-380
    • Braga-Neto, U.M.1    Dougherty, E.R.2
  • 43
    • 85153947273 scopus 로고
    • Nonlinear image interpolation using manifold learning
    • In G. Tesauro, D. S. Touretzky, and T. K. Leen, eds., The MIT Press
    • C. Bregler and S. M. Omoundro. Nonlinear image interpolation using manifold learning. In G. Tesauro, D. S. Touretzky, and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, The MIT Press, pp. 973-980. 1995.
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 973-980
    • Bregler, C.1    Omoundro, S.M.2
  • 44
    • 0003802343 scopus 로고
    • Classification and Regression Trees
    • Monterey, CA: Wadsworth.
    • L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Monterey, CA: Wadsworth. 1984.
    • (1984)
    • Breiman, L.1    Friedman, J.H.2    Olshen, R.A.3    Stone, C.J.4
  • 45
    • 0344995736 scopus 로고    scopus 로고
    • Continuous latent variable models for dimensionality reduction and sequential data reconstruction
    • PhD thesis. University of Sheffield, UK.
    • M. A. Carreira-Perpinñán. Continuous latent variable models for dimensionality reduction and sequential data reconstruction. PhD thesis. University of Sheffield, UK. 2001.
    • (2001)
    • Carreira-Perpinñán, M.A.1
  • 46
    • 0042856574 scopus 로고    scopus 로고
    • Using particle swarms for the development of QSAR models based on k-nearest neighbor and kernel regression
    • W. Cedeño and D. K. Agrafiotis. Using particle swarms for the development of QSAR models based on k-nearest neighbor and kernel regression. J. Comput. Aid. Mol. Des. 17: 255-263. 2003.
    • (2003) J. Comput. Aid. Mol. Des. , vol.17 , pp. 255-263
    • Cedeño, W.1    Agrafiotis, D.K.2
  • 47
    • 0024103809 scopus 로고
    • PRISM: An algorithm for inducing modular rules
    • J. Cendrowska. PRISM: An algorithm for inducing modular rules. Int. J. Man-Machine Studies 27 (4): 349-370. 1987.
    • (1987) Int. J. Man-Machine Studies , vol.27 , Issue.4 , pp. 349-370
    • Cendrowska, J.1
  • 49
    • 38149049048 scopus 로고    scopus 로고
    • Protecting rivers and streams by monitoring chemical concentrations and algae communities
    • ERUDIT: 3rd International Competition of Data Analysis by Intelligent Techniques (runner up).
    • R. Chan. Protecting rivers and streams by monitoring chemical concentrations and algae communities. ERUDIT: 3rd International Competition of Data Analysis by Intelligent Techniques (runner up). 1999.
    • (1999)
    • Chan, R.1
  • 50
    • 84990556423 scopus 로고
    • APACS: A System for Automatic Analysis and Classification of Conceptual Patterns
    • K. Chan and A. Wong. APACS: A System for Automatic Analysis and Classification of Conceptual Patterns. Comput. Intell. 6(3): 119-131. 1990.
    • (1990) Comput. Intell. , vol.6 , Issue.3 , pp. 119-131
    • Chan, K.1    Wong, A.2
  • 51
    • 0012280862 scopus 로고    scopus 로고
    • A new method for generating fuzzy rules from numerical data for handling classification problems
    • S. Chen, S. L. Lee, and C. Lee. A new method for generating fuzzy rules from numerical data for handling classification problems. Appl. Art. Intell. 15(7): 645-664. 2001.
    • (2001) Appl. Art. Intell. , vol.15 , Issue.7 , pp. 645-664
    • Chen, S.1    Lee, S.L.2    Lee, C.3
  • 52
    • 0037209813 scopus 로고    scopus 로고
    • Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment
    • W. C. Chen, N. B. Chang, and J. C. Chen. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment. Water Res. 37(1): 95-107. 2003.
    • (2003) Water Res. , vol.37 , Issue.1 , pp. 95-107
    • Chen, W.C.1    Chang, N.B.2    Chen, J.C.3
  • 53
    • 33646026682 scopus 로고    scopus 로고
    • Rough approximations on a complete completely distributive lattice with applications to generalized rough sets
    • D. Chen, W. X. Zhang, D. Yeung, and E. C. C. Tsang, Rough approximations on a complete completely distributive lattice with applications to generalized rough sets. Infor. Sci. 176(13): 1829-1848. 2006.
    • (2006) Infor. Sci. , vol.176 , Issue.13 , pp. 1829-1848
    • Chen, D.1    Zhang, W.X.2    Yeung, D.3    Tsang, E.C.C.4
  • 54
    • 0242322799 scopus 로고    scopus 로고
    • Rough set-aided keyword reduction for text categorisation
    • A. Chouchoulas and Q. Shen. Rough set-aided keyword reduction for text categorisation. Appl. Art. Intell. 15 (9): 843-873. 2001.
    • (2001) Appl. Art. Intell. , vol.15 , Issue.9 , pp. 843-873
    • Chouchoulas, A.1    Shen, Q.2
  • 57
    • 85015191605 scopus 로고
    • Machine Learning-Proceedings of the Fifth European Conference (EWSL-91)
    • Berlin: Springer
    • P. Clark and R. Boswell. Machine Learning-Proceedings of the Fifth European Conference (EWSL-91). Berlin: Springer, pp. 151-163. 1991.
    • (1991) , pp. 151-163
    • Clark, P.1    Boswell, R.2
  • 58
    • 34249966007 scopus 로고
    • The CN2 induction algorithm
    • P. Clark and T. Niblett. The CN2 induction algorithm. Mach. Learning J. 3(4): 261-283. 1989.
    • (1989) Mach. Learning J. , vol.3 , Issue.4 , pp. 261-283
    • Clark, P.1    Niblett, T.2
  • 62
    • 0003927095 scopus 로고    scopus 로고
    • Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases
    • Singapore: World Scientific.
    • O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena. Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. Singapore: World Scientific. 2001.
    • (2001)
    • Cordón, O.1    Herrera, F.2    Hoffmann, F.3    Magdalena, L.4
  • 63
    • 33750723494 scopus 로고    scopus 로고
    • A multiobjective genetic learning process for joint feature selection and granularity and contexts learning in fuzzy rule-based classification systems
    • In J. Casillas, O. Cordón, F. Herrera, and L. Magdalena, eds., Berlin: Springer
    • O. Cordón, M. J. del Jesus, F. Herrera, L. Magdalena, and P. Villar. A multiobjective genetic learning process for joint feature selection and granularity and contexts learning in fuzzy rule-based classification systems. In J. Casillas, O. Cordón, F. Herrera, and L. Magdalena, eds., Interpretability Issues in Fuzzy Modeling. Berlin: Springer, pp. 79-99. 2003.
    • (2003) Interpretability Issues in Fuzzy Modeling. , pp. 79-99
    • Cordón, O.1    Del Jesus, M.J.2    Herrera, F.3    Magdalena, L.4    Villar, P.5
  • 66
    • 0004065899 scopus 로고    scopus 로고
    • The Fuzzy Systems Handbook: A Practitioner's Guide to Building, Using and Maintaining Fuzzy Systems
    • San Diego: Academic Press.
    • E. Cox. The Fuzzy Systems Handbook: A Practitioner's Guide to Building, Using and Maintaining Fuzzy Systems. San Diego: Academic Press. 1999.
    • (1999)
    • Cox, E.1
  • 67
    • 0013326060 scopus 로고    scopus 로고
    • Feature selection for classification
    • M. Dash and H. Liu. Feature selection for classification. Intell. Data Anal. 1(3): 131-156. 1997.
    • (1997) Intell. Data Anal. , vol.1 , Issue.3 , pp. 131-156
    • Dash, M.1    Liu, H.2
  • 69
    • 0242302657 scopus 로고    scopus 로고
    • Consistency-based search in feature selection
    • M. Dash and H. Liu. Consistency-based search in feature selection. Art. Intell. 151(1-2): 155-176. 2003.
    • (2003) Art. Intell. , vol.151 , Issue.1-2 , pp. 155-176
    • Dash, M.1    Liu, H.2
  • 70
    • 84881072062 scopus 로고
    • A computing procedure for quantification theory
    • M. Davis and H. Putnam. A computing procedure for quantification theory. J. ACM 7(3): 201-215. 1960.
    • (1960) J. ACM , vol.7 , Issue.3 , pp. 201-215
    • Davis, M.1    Putnam, H.2
  • 71
    • 84919401135 scopus 로고
    • A machine program for theorem proving
    • M. Davis, G. Logemann, and D. Loveland. A machine program for theorem proving. Comm. ACM 5: 394-397. 1962.
    • (1962) Comm. ACM , vol.5 , pp. 394-397
    • Davis, M.1    Logemann, G.2    Loveland, D.3
  • 73
    • 42549146171 scopus 로고    scopus 로고
    • Fuzzy rough set based Web query expansion
    • International Workshop at WIIAT2005 (2005 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology), Piscataway, NJ: IEEE Press
    • M. De Cock and C. Cornelis. Fuzzy rough set based Web query expansion. Proceedings of Rough Sets and Soft Computing in Intelligent Agent and Web Technology. International Workshop at WIIAT2005 (2005 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology), Piscataway, NJ: IEEE Press, pp. 9-16, 2005.
    • (2005) Proceedings of Rough Sets and Soft Computing in Intelligent Agent and Web Technology. , pp. 9-16
    • De Cock, M.1    Cornelis, C.2
  • 76
    • 0001655091 scopus 로고
    • A generalization of Bayesian inference
    • P. Dempster. A generalization of Bayesian inference. J. Roy. Stat. Soc. B 30: 205-247. 1968.
    • (1968) J. Roy. Stat. Soc. B , vol.30 , pp. 205-247
    • Dempster, P.1
  • 77
    • 84928746885 scopus 로고
    • Pattern Recognition: A Statistical Approach
    • Englewood Cliffs, NJ: Prentice Hall.
    • P. Devijver and J. Kittler. Pattern Recognition: A Statistical Approach. Englewood Cliffs, NJ: Prentice Hall. 1982.
    • (1982)
    • Devijver, P.1    Kittler, J.2
  • 78
    • 0001493668 scopus 로고
    • Asymptotics of graphical projection pursuit
    • P. Diaconis and D. Freedman. Asymptotics of graphical projection pursuit. An. Stat. 12: 793-815. 1984.
    • (1984) An. Stat. , vol.12 , pp. 793-815
    • Diaconis, P.1    Freedman, D.2
  • 80
    • 0030143666 scopus 로고    scopus 로고
    • A survey of business failure with an emphasis on prediction methods and industrial applications
    • A. I. Dimitras, S. H. Zanakis, and C. Zopounidis. A survey of business failure with an emphasis on prediction methods and industrial applications. Eur. J. Oper. Res. 90: 487-513. 1996.
    • (1996) Eur. J. Oper. Res. , vol.90 , pp. 487-513
    • Dimitras, A.I.1    Zanakis, S.H.2    Zopounidis, C.3
  • 82
    • 0030082551 scopus 로고    scopus 로고
    • The ant system: Optimization by a colony of cooperating agents
    • M. Dorigo, V. Maniezzo, and A. Colorni. The ant system: Optimization by a colony of cooperating agents. IEEE Trans. Sys. Man Cyber. B 26(1): 29-41. 1996.
    • (1996) IEEE Trans. Sys. Man Cyber. B , vol.26 , Issue.1 , pp. 29-41
    • Dorigo, M.1    Maniezzo, V.2    Colorni, A.3
  • 84
    • 84957806180 scopus 로고    scopus 로고
    • Rough and fuzzy-rough classification methods implemented in RClass system
    • Lecture Notes in Computer Science, Springer-Verlag
    • G. Drwal. Rough and fuzzy-rough classification methods implemented in RClass system. In Proceedings of 2nd International Conference on Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science, 2005, Springer-Verlag, pp 152-159. 2000.
    • (2000) Proceedings of 2nd International Conference on Rough Sets and Current Trends in Computing. , pp. 152-159
    • Drwal, G.1
  • 85
    • 84963133436 scopus 로고
    • Rough fuzzy sets and fuzzy rough sets
    • D. Dubois and H. Prade. Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Sys. 17: 191-209. 1990.
    • (1990) Int. J. Gen. Sys. , vol.17 , pp. 191-209
    • Dubois, D.1    Prade, H.2
  • 86
    • 0038667021 scopus 로고
    • Putting rough sets and fuzzy sets together
    • In [340]
    • D. Dubois and H. Prade. Putting rough sets and fuzzy sets together. In [340], pp. 203-232. 1992.
    • (1992) , pp. 203-232
    • Dubois, D.1    Prade, H.2
  • 87
    • 84889404868 scopus 로고    scopus 로고
    • Combining evidence for effective information filtering
    • AAAI Spring Symposium on Machine Learning in Information access Technical Papers.
    • S. T. Dumais. Combining evidence for effective information filtering. AAAI Spring Symposium on Machine Learning in Information access Technical Papers. 1996.
    • (1996)
    • Dumais, S.T.1
  • 88
    • 0015644825 scopus 로고
    • A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters
    • J. C. Dunn. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cyber. 3(3): 32-57. 1973.
    • (1973) J. Cyber. , vol.3 , Issue.3 , pp. 32-57
    • Dunn, J.C.1
  • 89
    • 0010517693 scopus 로고    scopus 로고
    • Rough set data analysis
    • In A. Kent and J. G. Williams, eds.
    • I. Düntsch and G. Gediga. Rough set data analysis. In A. Kent and J. G. Williams, eds., Ency. Comput. Sci. Technol. 43 (28): 281-301. 2000.
    • (2000) Ency. Comput. Sci. Technol. , vol.43 , Issue.28 , pp. 281-301
    • Düntsch, I.1    Gediga, G.2
  • 90
    • 0011434736 scopus 로고    scopus 로고
    • Rough Set Data Analysis: A road to Non-invasive Knowledge Discovery
    • Bangor: Methodos.
    • I. Düntsch and G. Gediga. Rough Set Data Analysis: A road to Non-invasive Knowledge Discovery. Bangor: Methodos. 2000.
    • (2000)
    • Düntsch, I.1    Gediga, G.2
  • 92
    • 0003471831 scopus 로고
    • An Introduction to Linear Regression and Correlation
    • San Francisco: Freeman.
    • A. L. Edwards, An Introduction to Linear Regression and Correlation. San Francisco: Freeman. 1976.
    • (1976)
    • Edwards, A.L.1
  • 93
    • 79951856823 scopus 로고    scopus 로고
    • Generation of comprehensible decision trees through evolution of training data
    • Piscataway, NJ: IEEE Press
    • T. Endou and Q. Zhao. Generation of comprehensible decision trees through evolution of training data. In Proceedings of the 2002 IEEE World Congress on Computational Intelligence. Piscataway, NJ: IEEE Press, pp. 1221-1225. 2002.
    • (2002) Proceedings of the 2002 IEEE World Congress on Computational Intelligence. , pp. 1221-1225
    • Endou, T.1    Zhao, Q.2
  • 94
    • 84889349538 scopus 로고    scopus 로고
    • ERUDIT (European Network for Fuzzy Logic and Uncertainty Modeling in Information Technology). Protecting Rivers and Streams by Monitoring Chemical Concentrations and Algae Communities. 3rd International Competition.
    • ERUDIT (European Network for Fuzzy Logic and Uncertainty Modeling in Information Technology). Protecting Rivers and Streams by Monitoring Chemical Concentrations and Algae Communities. 3rd International Competition. 1999.
    • (1999)
  • 96
    • 9444272871 scopus 로고    scopus 로고
    • Rough Set Methodology in Clinical Practice: Controlled Hospital Trial of the MET System
    • Springer-Verlag Heiddberg RSCTC, LNAI 3066
    • K. Farion, W. Michalowski, R. Slowinski, S. Wilk, and S. Rubin. S. Tsumoto et al., eds., Rough Set Methodology in Clinical Practice: Controlled Hospital Trial of the MET System. Springer-Verlag Heiddberg RSCTC, LNAI 3066, pp. 805-814. 2004.
    • (2004) , pp. 805-814
    • Farion, K.1    Michalowski, W.2    Slowinski, R.3    Wilk, S.4    Tsumoto, S.5    Rubin, S.6
  • 97
    • 0002283033 scopus 로고    scopus 로고
    • From data mining to knowledge discovery in databases
    • U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery in databases. Art. Intell., 17(3): 37-54. 1996.
    • (1996) Art. Intell. , vol.17 , Issue.3 , pp. 37-54
    • Fayyad, U.1    Piatetsky-Shapiro, G.2    Smyth, P.3
  • 98
  • 99
    • 0000764772 scopus 로고
    • The use of multiple measurementsin taxonomic problems
    • R Fisher. The use of multiple measurementsin taxonomic problems. An. Eugen. 7: 179-188. 1936.
    • (1936) An. Eugen. , vol.7 , pp. 179-188
    • Fisher, R.1
  • 100
    • 0004168339 scopus 로고
    • Multivariate Statistics: A Practical Approach
    • Englewood Cliffs, NJ: Prentice Hall.
    • B. Flury and H. Riedwyl. Multivariate Statistics: A Practical Approach. Englewood Cliffs, NJ: Prentice Hall. 1988.
    • (1988)
    • Flury, B.1    Riedwyl, H.2
  • 102
    • 2942731012 scopus 로고    scopus 로고
    • An Extensive Empirical Study of Feature Selection Metrics for Text Classification
    • G Forman. An Extensive Empirical Study of Feature Selection Metrics for Text Classification. J. Mach. Learning Res. 3: 1289-1305. 2003.
    • (2003) J. Mach. Learning Res. , vol.3 , pp. 1289-1305
    • Forman, G.1
  • 103
    • 0016102310 scopus 로고
    • A projection pursuit algorithm for exploratory data analysis
    • J. H. Friedman and J. W. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. C 23(9): 881-890. 1974.
    • (1974) IEEE Trans. Comput. C , vol.23 , Issue.9 , pp. 881-890
    • Friedman, J.H.1    Tukey, J.W.2
  • 105
    • 0002432565 scopus 로고
    • Multivariate Adaptive Regression Splines
    • J. H. Friedman. Multivariate Adaptive Regression Splines. An. Stat. 19(1): 1-141. 1991.
    • (1991) An. Stat. , vol.19 , Issue.1 , pp. 1-141
    • Friedman, J.H.1
  • 106
    • 77957175435 scopus 로고
    • Probabilistic Models in Information Retrieval
    • N Fuhr. Probabilistic Models in Information Retrieval. Comput. J. 35(3): 243-55. 1992.
    • (1992) Comput. J. , vol.35 , Issue.3 , pp. 243-255
    • Fuhr, N.1
  • 107
    • 84889398770 scopus 로고    scopus 로고
    • Categorisation tool: Final prototype
    • Deliverable 4.3, Project LE4-8303 "EUROSEARCH," Commission of the European Communities.
    • N. Fuhr, N. Gövert, M. Lalmas, and F. Sebastiani. Categorisation tool: Final prototype. Deliverable 4.3, Project LE4-8303 "EUROSEARCH," Commission of the European Communities. 1999.
    • (1999)
    • Fuhr, N.1    Gövert, N.2    Lalmas, M.3    Sebastiani, F.4
  • 110
    • 18744394682 scopus 로고    scopus 로고
    • Evolutionary approaches to fuzzy modelling for classification
    • M. Galea, Q. Shen and J. Levine. Evolutionary approaches to fuzzy modelling for classification. Knowledge Eng. Rev. 19(1): 27-59. 2004.
    • (2004) Knowledge Eng. Rev. , vol.19 , Issue.1 , pp. 27-59
    • Galea, M.1    Shen, Q.2    Levine, J.3
  • 111
    • 0344548085 scopus 로고    scopus 로고
    • Linear and nonlinear data dimensionality reduction
    • Technical report. Massachusetts Institute of Technology.
    • D Gering. Linear and nonlinear data dimensionality reduction. Technical report. Massachusetts Institute of Technology. 2002.
    • (2002)
    • Gering, D.1
  • 112
    • 9444280894 scopus 로고    scopus 로고
    • Extracting Protein-Protein Interaction Sentences by Applying Rough Set Data Analysis
    • In S. Tsumoto et al., eds., Springer Verlag, LNAI 3066
    • F. Ginter, T. Pahikkala, S. Pyysalo, J. Boberg, J. Järvinen and T. Salakoski. In S. Tsumoto et al., eds., Extracting Protein-Protein Interaction Sentences by Applying Rough Set Data Analysis. Springer Verlag, LNAI 3066, pp. 780-785. 2004.
    • (2004) , pp. 780-785
    • Ginter, F.1    Pahikkala, T.2    Pyysalo, S.3    Boberg, J.4    Järvinen, J.5    Salakoski, T.6
  • 113
    • 0003615232 scopus 로고
    • Chaos: Making New Science
    • Penguin Books.
    • J Gleick. Chaos: Making New Science. Penguin Books. 1988.
    • (1988)
    • Gleick, J.1
  • 114
    • 0013024945 scopus 로고
    • Stock market analysis utilizing rough set theory
    • PhD thesis. Department of Computer Science, University of Regina, Canada.
    • R Golan. Stock market analysis utilizing rough set theory. PhD thesis. Department of Computer Science, University of Regina, Canada. 1995.
    • (1995)
    • Golan, R.1
  • 116
    • 0004556561 scopus 로고
    • Learning the structure of a fuzzy rule: A genetic approach
    • A. Gonzalez, R. Perez, and J. L. Verdegay. Learning the structure of a fuzzy rule: A genetic approach. Fuzzy Sys. Art. Intell. 3(1): 57-70. 1994.
    • (1994) Fuzzy Sys. Art. Intell. , vol.3 , Issue.1 , pp. 57-70
    • Gonzalez, A.1    Perez, R.2    Verdegay, J.L.3
  • 117
    • 27844601150 scopus 로고    scopus 로고
    • Fuzzy rough sets and multiple-premise gradual decision rules
    • S. Greco, M. Inuiguchi, and R. Slowinski. Fuzzy rough sets and multiple-premise gradual decision rules. Int. J. Approx. Reason. 41: 179-211. 2005.
    • (2005) Int. J. Approx. Reason. , vol.41 , pp. 179-211
    • Greco, S.1    Inuiguchi, M.2    Slowinski, R.3
  • 118
    • 33947248625 scopus 로고    scopus 로고
    • A new proposal for fuzzy rough approximations and gradual decision rule representation
    • Transactions on Rough Sets II. Lecture Notes in Computer Science, Berlin Springer
    • S. Greco, M. Inuiguchi, and R. Slowinski. A new proposal for fuzzy rough approximations and gradual decision rule representation. Transactions on Rough Sets II. Lecture Notes in Computer Science, Vol. 3135. Berlin Springer, pp. 319-342. 2004.
    • (2004) , vol.3135 , pp. 319-342
    • Greco, S.1    Inuiguchi, M.2    Slowinski, R.3
  • 119
    • 0002135094 scopus 로고
    • LERS-A system for learning from examples based on rough sets
    • In R. Slowinski, ed., Dordrecht: Kluwer Academic
    • J.W. Grzymala-Busse. LERS-A system for learning from examples based on rough sets. In R. Slowinski, ed., Intelligent Decision Support. Dordrecht: Kluwer Academic, pp. 3-18. 1992.
    • (1992) Intelligent Decision Support. , pp. 3-18
    • Grzymala-Busse, J.W.1
  • 120
    • 18944377438 scopus 로고    scopus 로고
    • A comparison of three strategies to rule induction from data with numerical attributes
    • European Joint Conferences on Theory and Practice of Software. Elsevier
    • J. W. Grzymala-Busse. A comparison of three strategies to rule induction from data with numerical attributes. Proceedings of the International Workshop on Rough Sets in Knowledge Discovery. European Joint Conferences on Theory and Practice of Software. Elsevier, pp. 132-140. 2003.
    • (2003) Proceedings of the International Workshop on Rough Sets in Knowledge Discovery. , pp. 132-140
    • Grzymala-Busse, J.W.1
  • 121
    • 35048837610 scopus 로고    scopus 로고
    • Three strategies to rule induction from data with numerical attributes
    • Berlin: Springer
    • J. W. Grzymala-Busse. Three strategies to rule induction from data with numerical attributes. Transactions on Rough Sets II , Berlin: Springer, pp. 54-62. 2004.
    • (2004) Transactions on Rough Sets II , pp. 54-62
    • Grzymala-Busse, J.W.1
  • 123
    • 9444282077 scopus 로고    scopus 로고
    • Feature subset selection based on relative dependency between attributes
    • Rough Sets and Current Trends in Computing: 4th International Conference, Uppsala, Sweden, June 1-5
    • J. Han, X. Hu, and T. Y. Lin. Feature subset selection based on relative dependency between attributes. Rough Sets and Current Trends in Computing: 4th International Conference, Uppsala, Sweden, June 1-5, pp. 176-185. 2004.
    • (2004) , pp. 176-185
    • Han, J.1    Hu, X.2    Lin, T.Y.3
  • 124
    • 0344004828 scopus 로고    scopus 로고
    • Feature selection for optimized skin tumor recognition using genetic algorithms
    • H. Handels, T. Roß, J. Kreusch, H. H. Wolff, and S. Pöpple. Feature selection for optimized skin tumor recognition using genetic algorithms. Art. Intell. Med. 16(3): 283-297. 1999.
    • (1999) Art. Intell. Med. , vol.16 , Issue.3 , pp. 283-297
    • Handels, H.1    Roß, T.2    Kreusch, J.3    Wolff, H.H.4    Pöpple, S.5
  • 125
    • 0001138328 scopus 로고
    • A k-means clustering algorithm
    • J. A. Hartigan and M. A. Wong. A k-means clustering algorithm. Appl. Stat. 28(1): 100-108. 1979.
    • (1979) Appl. Stat. , vol.28 , Issue.1 , pp. 100-108
    • Hartigan, J.A.1    Wong, M.A.2
  • 127
    • 33748894279 scopus 로고    scopus 로고
    • Genetic fuzzy systems: Status, critical considerations and future directions
    • F. Herrera. Genetic fuzzy systems: Status, critical considerations and future directions. Int. J. Comput. Intell. Res. 1(1): 59-67. 2005.
    • (2005) Int. J. Comput. Intell. Res. , vol.1 , Issue.1 , pp. 59-67
    • Herrera, F.1
  • 128
    • 84942920584 scopus 로고    scopus 로고
    • Towards the Classification of Musical Works: A Rough Set Approach
    • In J. J. Alpigini et al., eds., LNAI 2475, Springer Verlag
    • M. P. Hippe. In J. J. Alpigini et al., eds., Towards the Classification of Musical Works: A Rough Set Approach. LNAI 2475, Springer Verlag, pp. 546-553. 2002.
    • (2002) , pp. 546-553
    • Hippe, M.P.1
  • 129
    • 3042712702 scopus 로고    scopus 로고
    • Rough clustering and its application to medicine
    • S. Hirano and S. Tsumoto. Rough clustering and its application to medicine. J. Info. Sci. 124: 125-137, 2000.
    • (2000) J. Info. Sci. , vol.124 , pp. 125-137
    • Hirano, S.1    Tsumoto, S.2
  • 130
    • 0003467997 scopus 로고
    • Industrial Applications of Fuzzy Technology
    • Tokyo: Springer.
    • K. Hirota, ed. Industrial Applications of Fuzzy Technology. Tokyo: Springer. 1993.
    • (1993)
    • Hirota, K.1
  • 132
    • 38249027581 scopus 로고
    • Quotients with respect to similarity relations
    • U. Höhle. Quotients with respect to similarity relations. Fuzzy Sets Sys. 27(1): 31-44. 1988.
    • (1988) Fuzzy Sets Sys. , vol.27 , Issue.1 , pp. 31-44
    • Höhle, U.1
  • 133
    • 0003463297 scopus 로고
    • Adaptation In Natural and Artificial Systems
    • Ann Arbor: University of Michigan Press
    • J. Holland. Adaptation In Natural and Artificial Systems. Ann Arbor: University of Michigan Press, 1975.
    • (1975)
    • Holland, J.1
  • 134
    • 0001666176 scopus 로고
    • Cognitive systems based on adaptive algorithms
    • In D. A. Waterman F. Hayes-Roth., New York: Academic Press.
    • J. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman F. Hayes-Roth. Pattern-Directed Inference Systems. New York: Academic Press. p. 49. 1978.
    • (1978) Pattern-Directed Inference Systems. , pp. 49
    • Holland, J.1    Reitman, J.S.2
  • 135
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • R. C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learn. 11(1): 63-90. 1993.
    • (1993) Machine Learn. , vol.11 , Issue.1 , pp. 63-90
    • Holte, R.C.1
  • 136
    • 33847746961 scopus 로고    scopus 로고
    • Learning with hierarchical quantitative attributes by fuzzy rough sets
    • Advances in Intelligent Systems Research, Atlantic Press.
    • T. P. Hong, Y. L. Liou, and S. L. Wang. Learning with hierarchical quantitative attributes by fuzzy rough sets. In Proceedings of Joint Conference on Information Sciences, Advances in Intelligent Systems Research, Atlantic Press. 2006.
    • (2006) Proceedings of Joint Conference on Information Sciences
    • Hong, T.P.1    Liou, Y.L.2    Wang, S.L.3
  • 137
    • 0033170342 scopus 로고    scopus 로고
    • Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
    • H. H. Hoos and T. Stützle. Towards a characterisation of the behaviour of stochastic local search algorithms for SAT. Artificial Intelligence 112: 213-232. 1999.
    • (1999) Artificial Intelligence , vol.112 , pp. 213-232
    • Hoos, H.H.1    Stützle, T.2
  • 138
    • 0003630531 scopus 로고    scopus 로고
    • Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition
    • New York: Wiley.
    • F. Hoppner, R. Kruse, F. Klawonn, and T. Runkler. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. New York: Wiley. 2000.
    • (2000)
    • Hoppner, F.1    Kruse, R.2    Klawonn, F.3    Runkler, T.4
  • 139
    • 0002523367 scopus 로고    scopus 로고
    • A new interpolative reasoning method in sparse rule-based systems
    • W. H. Hsiao, S. M. Chen, and C. H. Lee. A new interpolative reasoning method in sparse rule-based systems. Fuzzy Sets Sys. 93: 17-22. 1998.
    • (1998) Fuzzy Sets Sys. , vol.93 , pp. 17-22
    • Hsiao, W.H.1    Chen, S.M.2    Lee, C.H.3
  • 140
    • 32644440353 scopus 로고    scopus 로고
    • Information-preserving hybrid data reduction based on fuzzy-rough techniques
    • Q. Hu, D. Yu, and Z. Xie. Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recog. Lett. 27(5): 414-423, 2006.
    • (2006) Pattern Recog. Lett. , vol.27 , Issue.5 , pp. 414-423
    • Hu, Q.1    Yu, D.2    Xie, Z.3
  • 141
    • 33645801018 scopus 로고    scopus 로고
    • Fuzzy probabilistic approximation spaces and their information measures
    • Q. Hu, D. Yu, Z. Xie, and J. Liu. Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans. Fuzzy Sys. 14(2): 191-201. 2006.
    • (2006) IEEE Trans. Fuzzy Sys. , vol.14 , Issue.2 , pp. 191-201
    • Hu, Q.1    Yu, D.2    Xie, Z.3    Liu, J.4
  • 144
    • 33645801813 scopus 로고    scopus 로고
    • Fuzzy interpolative reasoning via scale and move transformation
    • Z. Huang and Q. Shen. Fuzzy interpolative reasoning via scale and move transformation. IEEE Trans. Fuzzy Sys. 14(2): 340-359. 2006.
    • (2006) IEEE Trans. Fuzzy Sys. , vol.14 , Issue.2 , pp. 340-359
    • Huang, Z.1    Shen, Q.2
  • 145
    • 40549106835 scopus 로고    scopus 로고
    • Fuzzy interpolative and extrapolative reasoning: A practical approach
    • Forthcoming
    • Z. Huang and Q. Shen. Fuzzy interpolative and extrapolative reasoning: A practical approach. IEEE Trans. Fuzzy Sys., 16(1): 13-28. 2008. Forthcoming.
    • (2008) IEEE Trans. Fuzzy Sys. , vol.16 , Issue.1 , pp. 13-28
    • Huang, Z.1    Shen, Q.2
  • 146
    • 0004064575 scopus 로고
    • Experiments in Induction
    • New York: Academic Press.
    • E. Hunt, J. Martin, and P. Stone. Experiments in Induction. New York: Academic Press. 1966.
    • (1966)
    • Hunt, E.1    Martin, J.2    Stone, P.3
  • 147
    • 0038729567 scopus 로고    scopus 로고
    • Learning rule-based models of biological process from gene expression time profiles using gene ontology
    • T. Hvidsten, A. Lægreid, and J. Komorowski. Learning rule-based models of biological process from gene expression time profiles using gene ontology. Bioinformatics 19: 1116-1123. 2003.
    • (2003) Bioinformatics , vol.19 , pp. 1116-1123
    • Hvidsten, T.1    Lægreid, A.2    Komorowski, J.3
  • 148
    • 84889372424 scopus 로고    scopus 로고
    • 1.5 Million Pages Added to Web Each Day, Says Research Company
    • Internet News., September 1st, Available at
    • Internet News. "1.5 Million Pages Added to Web Each Day, Says Research Company," September 1st, 1998. Available at http://www.internetnews.com/bus-news/article.php/37891.
    • (1998)
  • 149
    • 0002197262 scopus 로고
    • Distributed representation of fuzzy rules and its application to pattern classification
    • H. Ishibuchi, K. Nozaki, and H. Tanaka. Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Sys. 52(1): 21-32. 1992.
    • (1992) Fuzzy Sets Sys. , vol.52 , Issue.1 , pp. 21-32
    • Ishibuchi, H.1    Nozaki, K.2    Tanaka, H.3
  • 150
    • 0029359001 scopus 로고
    • Selecting fuzzy if-then rules for classification problems using genetic algorithms
    • H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Sys. 3(3): 260-270. 1995.
    • (1995) IEEE Trans. Fuzzy Sys. , vol.3 , Issue.3 , pp. 260-270
    • Ishibuchi, H.1    Nozaki, K.2    Yamamoto, N.3    Tanaka, H.4
  • 151
    • 0032597810 scopus 로고    scopus 로고
    • Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems
    • H. Ishibuchi, T. Nakashima, and T. Murata. Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans. Sys. Man Cyber. B 29(5): 601-618. 1999.
    • (1999) IEEE Trans. Sys. Man Cyber. B , vol.29 , Issue.5 , pp. 601-618
    • Ishibuchi, H.1    Nakashima, T.2    Murata, T.3
  • 152
    • 0346781550 scopus 로고    scopus 로고
    • Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining
    • H. Ishibuchi and T. Yamamoto. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Sys. 141(1): 59-88. 2004.
    • (2004) Fuzzy Sets Sys. , vol.141 , Issue.1 , pp. 59-88
    • Ishibuchi, H.1    Yamamoto, T.2
  • 153
    • 15744380274 scopus 로고    scopus 로고
    • Multi-objective evolutionary design of fuzzy rule-based systems
    • Piscataway, NJ: IEEE Press
    • H. Ishibuchi and T. Yamamoto. Multi-objective evolutionary design of fuzzy rule-based systems. IEEE International Conference on Systems, Man and Cybernetics, Vol. 3. Piscataway, NJ: IEEE Press, pp. 2362-2367. 2004.
    • (2004) IEEE International Conference on Systems, Man and Cybernetics , vol.3 , pp. 2362-2367
    • Ishibuchi, H.1    Yamamoto, T.2
  • 155
    • 0027266182 scopus 로고
    • Functional equivalence between radial basis function networks and fuzzy inference systems
    • J.-S. R. Jang, Y. C. Lee, and C.-T. Sun. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Net. 4(1): 156-159. 1993.
    • (1993) IEEE Trans. Neural Net. , vol.4 , Issue.1 , pp. 156-159
    • Jang, J.-S.R.1    Lee, Y.C.2    Sun, C.-T.3
  • 156
    • 0003753097 scopus 로고    scopus 로고
    • Neuro-Fuzzy and Soft Computing, Matlab Curriculum
    • Englewood Cliffs, NJ: Prentice Hall.
    • J.-S. R. Jang, C.-T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing, Matlab Curriculum. Englewood Cliffs, NJ: Prentice Hall. 1997.
    • (1997)
    • Jang, J.-S.R.1    Sun, C.-T.2    Mizutani, E.3
  • 157
    • 0031996838 scopus 로고    scopus 로고
    • Fuzzy decision trees: Issues and methods
    • C. Z. Janikow. Fuzzy decision trees: Issues and methods. IEEE Trans. Sys. Man Cyber. B 28(1): 1-14. 1998.
    • (1998) IEEE Trans. Sys. Man Cyber. B , vol.28 , Issue.1 , pp. 1-14
    • Janikow, C.Z.1
  • 158
    • 0031105969 scopus 로고    scopus 로고
    • Feature subset selection for classification of histological images
    • J. Jelonek and J. Stefanowski. Feature subset selection for classification of histological images. Art. Intell. Med. 9(3): 227-239. 1997.
    • (1997) Art. Intell. Med. , vol.9 , Issue.3 , pp. 227-239
    • Jelonek, J.1    Stefanowski, J.2
  • 159
    • 9444259287 scopus 로고    scopus 로고
    • Inducing jury's preferences in terms of acoustic features of violin sounds
    • In L. Rutkowski et al., eds., Lecture Notes in Artificial Intelligence 3070 , Springer-Verlag
    • J. Jelonek, E. Lukasik, A. Naganowski, and R. Slowinski. Inducing jury's preferences in terms of acoustic features of violin sounds. In L. Rutkowski et al., eds., Lecture Notes in Artificial Intelligence 3070 , Springer-Verlag, pp. 492-497. 2004.
    • (2004) , pp. 492-497
    • Jelonek, J.1    Lukasik, E.2    Naganowski, A.3    Slowinski, R.4
  • 160
    • 33846273453 scopus 로고    scopus 로고
    • Performing Feature Selection with ACO
    • In A. Abraham, C. Grosan and V. Ramos, eds.
    • R. Jensen. Performing Feature Selection with ACO. In A. Abraham, C. Grosan and V. Ramos, eds., Swarm Intelligence and Data Mining. Berlin: Springer, pp. 45-73. 2006.
    • (2006) Swarm Intelligence and Data Mining. , pp. 45-73
    • Jensen, R.1
  • 162
    • 0348170835 scopus 로고    scopus 로고
    • Fuzzy-rough attribute reduction with application to Web categorization
    • R. Jensen and Q. Shen. Fuzzy-rough attribute reduction with application to Web categorization. Fuzzy Sets Sys. 141(3): 469-485. 2004.
    • (2004) Fuzzy Sets Sys. , vol.141 , Issue.3 , pp. 469-485
    • Jensen, R.1    Shen, Q.2
  • 163
    • 10944249572 scopus 로고    scopus 로고
    • Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches
    • R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Trans. Knowledge Data Eng. 16(12): 1457-1471. 2004.
    • (2004) IEEE Trans. Knowledge Data Eng. , vol.16 , Issue.12 , pp. 1457-1471
    • Jensen, R.1    Shen, Q.2
  • 164
    • 9644262464 scopus 로고    scopus 로고
    • Fuzzy-rough data reduction with ant colony optimization
    • R. Jensen and Q. Shen. Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets Sys. 149(1): 5-20. 2005.
    • (2005) Fuzzy Sets Sys. , vol.149 , Issue.1 , pp. 5-20
    • Jensen, R.1    Shen, Q.2
  • 167
    • 84899388090 scopus 로고    scopus 로고
    • Rough set-based feature selection: A review
    • In A. E. Hassanien, Z. Suraj, D. Slezak, and P. Lingras, eds., IGI Global Press
    • R. Jensen and Q. Shen. Rough set-based feature selection: A review. In A. E. Hassanien, Z. Suraj, D. Slezak, and P. Lingras, eds., Rough Computing: Theories, Technologies and Applications. IGI Global Press pp. 70-107. 2007.
    • (2007) Rough Computing: Theories, Technologies and Applications. , pp. 70-107
    • Jensen, R.1    Shen, Q.2
  • 168
    • 33947421283 scopus 로고    scopus 로고
    • Fuzzy-Rough Sets Assisted Attribute Selection
    • R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Trans. Fuzzy Sys. 15(1): 73-89. 2007.
    • (2007) IEEE Trans. Fuzzy Sys. , vol.15 , Issue.1 , pp. 73-89
    • Jensen, R.1    Shen, Q.2
  • 169
    • 84889379414 scopus 로고    scopus 로고
    • New approaches to fuzzy-rough feature selection
    • forthcoming
    • R. Jensen and Q. Shen. New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Sys., forthcoming.
    • IEEE Trans. Fuzzy Sys.
    • Jensen, R.1    Shen, Q.2
  • 170
    • 0033704546 scopus 로고    scopus 로고
    • Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement
    • Y. Jin. Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Sys. 8(2): 212-221. 2000.
    • (2000) IEEE Trans. Fuzzy Sys. , vol.8 , Issue.2 , pp. 212-221
    • Jin, Y.1
  • 172
    • 0003946510 scopus 로고
    • Principal Component Analysis
    • Berlin: Springer.
    • L. T. Jolliffe. Principal Component Analysis. Berlin: Springer. 1986.
    • (1986)
    • Jolliffe, L.T.1
  • 173
    • 0003646291 scopus 로고    scopus 로고
    • Theoretical Aspects of Evolutionary Computing
    • Berlin: Springer.
    • L. Kallel, B. Naudts, and A. Rogers, eds. Theoretical Aspects of Evolutionary Computing. Berlin: Springer. 2001.
    • (2001)
    • Kallel, L.1    Naudts, B.2    Rogers, A.3
  • 174
    • 0003430544 scopus 로고
    • Finding Groups in Data
    • New York: Wiley.
    • L. Kaufman and P. J. Rousseeuw. Finding Groups in Data. New York: Wiley. 1990.
    • (1990)
    • Kaufman, L.1    Rousseeuw, P.J.2
  • 177
    • 26444479778 scopus 로고
    • Optimization by simulated annealing
    • S. Kirkpatrick, C. Gelatt and M. Vecchi. Optimization by simulated annealing. Science 220(4598): 671-680. 1983.
    • (1983) Science , vol.220 , Issue.4598 , pp. 671-680
    • Kirkpatrick, S.1    Gelatt, C.2    Vecchi, M.3
  • 178
    • 84995347371 scopus 로고    scopus 로고
    • Myths about rough set theory
    • W. W. Koczkodaj, M. Orlowski, and V. W. Marek. Myths about rough set theory. Comm. ACM 41(11): 102-103. 1998.
    • (1998) Comm. ACM , vol.41 , Issue.11 , pp. 102-103
    • Koczkodaj, W.W.1    Orlowski, M.2    Marek, V.W.3
  • 179
    • 0008573506 scopus 로고
    • Approximate reasoning by linear rule interpolation and general approximation
    • L. T. Kóczy and K. Hirota. Approximate reasoning by linear rule interpolation and general approximation. Int. J. Approx. Reason. 9: 197-225. 1993.
    • (1993) Int. J. Approx. Reason. , vol.9 , pp. 197-225
    • Kóczy, L.T.1    Hirota, K.2
  • 183
    • 0032896727 scopus 로고    scopus 로고
    • The forensic significance of glass composition and refractive index measurements
    • R. D. Koons and J. Buscaglia. The forensic significance of glass composition and refractive index measurements. J. Forensic Sci. 44(3): 496-503. 1999.
    • (1999) J. Forensic Sci. , vol.44 , Issue.3 , pp. 496-503
    • Koons, R.D.1    Buscaglia, J.2
  • 184
    • 0036112580 scopus 로고    scopus 로고
    • Interpretation of glass composition measurements: the effects of match criteria on discrimination capability
    • R. D. Koons and J. Buscaglia. Interpretation of glass composition measurements: the effects of match criteria on discrimination capability. J. Forensic Sci. 47(3): 505-512. 2002.
    • (2002) J. Forensic Sci. , vol.47 , Issue.3 , pp. 505-512
    • Koons, R.D.1    Buscaglia, J.2
  • 185
    • 0022982937 scopus 로고
    • Fuzzy entropy and conditioning
    • B. Kosko. Fuzzy entropy and conditioning. Info Sci. 40(2): 165-174. 1986.
    • (1986) Info Sci. , vol.40 , Issue.2 , pp. 165-174
    • Kosko, B.1
  • 186
    • 9444222712 scopus 로고    scopus 로고
    • Processing of musical data employing rough sets and artificial neural networks
    • In S. Tsumoto et al., eds., LNAI Springer-Verlag
    • B. Kostek, P. Szczuko, and P. Zwan. Processing of musical data employing rough sets and artificial neural networks. In S. Tsumoto et al., eds., LNAI Springer-Verlag, pp. 539-548. 2004.
    • (2004) , pp. 539-548
    • Kostek, B.1    Szczuko, P.2    Zwan, P.3
  • 187
    • 0026113980 scopus 로고
    • Nonlinear principal component analysis using autoassociative neural networks
    • M. A. Kramer. Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37(2): 233-243. 1991.
    • (1991) AIChE J. , vol.37 , Issue.2 , pp. 233-243
    • Kramer, M.A.1
  • 188
    • 0041654220 scopus 로고
    • Multidimensional scaling by optimizing goodness of fit to a non-metric hypothesis
    • J. B. Kruskal. Multidimensional scaling by optimizing goodness of fit to a non-metric hypothesis. Psychometrika 29(1): 1-27. 1964.
    • (1964) Psychometrika , vol.29 , Issue.1 , pp. 1-27
    • Kruskal, J.B.1
  • 189
    • 41149114280 scopus 로고
    • Maintenance of reducts in the variable precision rough sets model
    • ICS Research Report 31/94. Warsaw University of Technology.
    • M. Kryszkiewicz. Maintenance of reducts in the variable precision rough sets model. ICS Research Report 31/94. Warsaw University of Technology. 1994.
    • (1994)
    • Kryszkiewicz, M.1
  • 190
    • 0033640901 scopus 로고    scopus 로고
    • Comparison of algorithms that select features for pattern classifiers
    • M. Kudo and J. Skalansky. Comparison of algorithms that select features for pattern classifiers. Pattern Recog. 33(1): 25-41. 2000.
    • (2000) Pattern Recog. , vol.33 , Issue.1 , pp. 25-41
    • Kudo, M.1    Skalansky, J.2
  • 191
    • 38249009996 scopus 로고
    • Fuzzy rough sets: application to feature selection
    • L.I. Kuncheva. Fuzzy rough sets: application to feature selection. Fuzzy Sets Sys. 51(2): 147-153. 1992.
    • (1992) Fuzzy Sets Sys. , vol.51 , Issue.2 , pp. 147-153
    • Kuncheva, L.I.1
  • 193
    • 84889275167 scopus 로고    scopus 로고
    • Accounting issues surrounding the split capital investment trust crisis of 2001 and 2002
    • MA thesis. University of Edinburgh.
    • I. M. F. Langlands. Accounting issues surrounding the split capital investment trust crisis of 2001 and 2002. MA thesis. University of Edinburgh. 2004.
    • (2004)
    • Langlands, I.M.F.1
  • 194
    • 85061066913 scopus 로고
    • Selection of relevant features in machine learning
    • Washington: AAAI
    • P. Langley. Selection of relevant features in machine learning. In Proceedings of AAAI Fall Symposium on Relevance. Washington: AAAI, pp. 1-5. 1994.
    • (1994) Proceedings of AAAI Fall Symposium on Relevance. , pp. 1-5
    • Langley, P.1
  • 195
  • 196
    • 18744399246 scopus 로고    scopus 로고
    • Computational logic and machine learning: A roadmap for inductive logic programming
    • Technical report. J. Stefan Institute, Ljubljana, Slovenia.
    • N. Lavrac. Computational logic and machine learning: A roadmap for inductive logic programming. Technical report. J. Stefan Institute, Ljubljana, Slovenia. 1998.
    • (1998)
    • Lavrac, N.1
  • 199
    • 9444291354 scopus 로고    scopus 로고
    • Classification of swallowing sound signals: A rough set approach
    • In S. Tsumoto et al., eds., RSCTC, LNAI Springer Verlag
    • L. Lazareckl and S. Ramanna. Classification of swallowing sound signals: A rough set approach. In S. Tsumoto et al., eds., RSCTC, LNAI Springer Verlag, pp. 679-684. 2004.
    • (2004) , pp. 679-684
    • Lazareckl, L.1    Ramanna, S.2
  • 200
    • 85002377847 scopus 로고
    • Genetic Algorithms as a Strategy for Feature Selection
    • R. Leardi, R. Boggia and M. Terrile. Genetic Algorithms as a Strategy for Feature Selection. J. Chemomet. 6(5): 267-28. 1992.
    • (1992) J. Chemomet. , vol.6 , Issue.5 , pp. 267-228
    • Leardi, R.1    Boggia, R.2    Terrile, M.3
  • 201
    • 0345019845 scopus 로고    scopus 로고
    • Genetic algorithms applied to feature selection in PLS regression: How and when to use them
    • R. Leardi and A. L. Gonzalez. Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemomet. Intell. Lab. Sys. 41(2): 195-207. 1998.
    • (1998) Chemomet. Intell. Lab. Sys. , vol.41 , Issue.2 , pp. 195-207
    • Leardi, R.1    Gonzalez, A.L.2
  • 202
    • 0033295883 scopus 로고    scopus 로고
    • PowerBookmarks: A system for personalizable Web information organization, sharing, and management
    • W. S. Li, Q. Vu, D. Agrawal, Y. Hara, and H. Takano. PowerBookmarks: A system for personalizable Web information organization, sharing, and management. Computer Networks: Int. J. Comput. Telecomm. Network. 31(11-16): 1375-1389. 1999.
    • (1999) Computer Networks: Int. J. Comput. Telecomm. Network. , vol.31 , Issue.11-16 , pp. 1375-1389
    • Li, W.S.1    Vu, Q.2    Agrawal, D.3    Hara, Y.4    Takano, H.5
  • 205
    • 0034491033 scopus 로고    scopus 로고
    • Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing
    • P Lingras. Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing. Neurocomputing, 36: 29-44, 2001.
    • (2001) Neurocomputing , vol.36 , pp. 29-44
    • Lingras, P.1
  • 206
    • 0035415091 scopus 로고    scopus 로고
    • Applications of rough genetic algorithms
    • P. Lingras and C. Davies. Applications of rough genetic algorithms. Comput. Intell. 17(3): 435-445. 2001.
    • (2001) Comput. Intell. , vol.17 , Issue.3 , pp. 435-445
    • Lingras, P.1    Davies, C.2
  • 209
    • 11144312947 scopus 로고    scopus 로고
    • Interval set clustering of Web users using modified Kohonen self-organizing maps based on the properties of rough sets
    • P. Lingras, M. Hogo, and M. Snorek. Interval set clustering of Web users using modified Kohonen self-organizing maps based on the properties of rough sets. Web Intell. Agent Sys. 2(3): 217-230. 2004.
    • (2004) Web Intell. Agent Sys. , vol.2 , Issue.3 , pp. 217-230
    • Lingras, P.1    Hogo, M.2    Snorek, M.3
  • 210
    • 3042789571 scopus 로고    scopus 로고
    • Interval set clustering of Web users with rough K-means
    • P. Lingras and C. West. Interval set clustering of Web users with rough K-means. J. Intell. Info. Sys. 23(1): 5-16. 2004.
    • (2004) J. Intell. Info. Sys. , vol.23 , Issue.1 , pp. 5-16
    • Lingras, P.1    West, C.2
  • 214
    • 0003602164 scopus 로고    scopus 로고
    • Feature Extraction, Construction and Selection: A Data Mining Perspective
    • Dordrecht: Kluwer Academic.
    • H. Liu, H. Motoda, eds. Feature Extraction, Construction and Selection: A Data Mining Perspective. Dordrecht: Kluwer Academic. 1998.
    • (1998)
    • Liu, H.1    Motoda, H.2
  • 215
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating feature selection algorithms for classification and clustering
    • H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge Data Eng. 17(3): 1-12. 2005.
    • (2005) IEEE Trans. Knowledge Data Eng. , vol.17 , Issue.3 , pp. 1-12
    • Liu, H.1    Yu, L.2
  • 217
    • 0009302442 scopus 로고    scopus 로고
    • Automatically organizing bookmarks per contents
    • Y. S. Maarek and I. Z. Ben Shaul. Automatically organizing bookmarks per contents. Comput. Net. ISDN Sys. 28 (7-11): 1321-1333. 1996.
    • (1996) Comput. Net. ISDN Sys. , vol.28 , Issue.7-11 , pp. 1321-1333
    • Maarek, Y.S.1    Ben Shaul, I.Z.2
  • 219
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • Berkeley: University of California Press
    • J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, Berkeley: University of California Press, pp. 281-297. 1967.
    • (1967) Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability , vol.1 , pp. 281-297
    • MacQueen, J.B.1
  • 220
    • 0004263139 scopus 로고
    • The Fractal Geometry of Nature
    • San Francisco: Freeman.
    • B. Mandelbrot. The Fractal Geometry of Nature. San Francisco: Freeman. 1982.
    • (1982)
    • Mandelbrot, B.1
  • 221
    • 0033312934 scopus 로고    scopus 로고
    • The ant system applied to the quadratic assignment problem
    • V. Maniezzo and A. Colorni. The ant system applied to the quadratic assignment problem. Knowledge Data Eng. 11(5): 769-778. 1999.
    • (1999) Knowledge Data Eng. , vol.11 , Issue.5 , pp. 769-778
    • Maniezzo, V.1    Colorni, A.2
  • 222
    • 3142683090 scopus 로고    scopus 로고
    • Continuous failure diagnosis for assembly systems using rough set approach
    • K. Mannar and D. Ceglarek. Continuous failure diagnosis for assembly systems using rough set approach. An. CIRP 53: 39-42. 2004.
    • (2004) An. CIRP , vol.53 , pp. 39-42
    • Mannar, K.1    Ceglarek, D.2
  • 223
    • 0003607151 scopus 로고
    • Multivariate Analysis
    • New York: Academic Press.
    • K. V. Mardia, J. T. Kent, and J. M. Bibby. Multivariate Analysis. New York: Academic Press. 1979.
    • (1979)
    • Mardia, K.V.1    Kent, J.T.2    Bibby, J.M.3
  • 224
    • 0036684175 scopus 로고    scopus 로고
    • From approximative to descriptive fuzzy classifiers
    • J. G. Marin-Blázquez and Q. Shen. From approximative to descriptive fuzzy classifiers. IEEE Trans. Fuzzy Sys. 10(4): 484-497. 2002.
    • (2002) IEEE Trans. Fuzzy Sys. , vol.10 , Issue.4 , pp. 484-497
    • Marin-Blázquez, J.G.1    Shen, Q.2
  • 225
    • 34547123220 scopus 로고    scopus 로고
    • Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges
    • In Casillas et al., eds., Springer, Berlin
    • J. G. Marin-Blázquez and Q. Shen. Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges. In Casillas et al., eds., Interpretability Issues in Fuzzy Modelling. Studies in Fuzziness and Soft Computing, Vol. 128, Springer, Berlin, pp. 25-53. 2003.
    • (2003) Interpretability Issues in Fuzzy Modelling. Studies in Fuzziness and Soft Computing , vol.128 , pp. 25-53
    • Marin-Blázquez, J.G.1    Shen, Q.2
  • 226
    • 1642603452 scopus 로고    scopus 로고
    • Classification of multispectral images through a rough-fuzzy neural network
    • C.-W. Mao, S.-H. Liu, and J.-S. Lin. Classification of multispectral images through a rough-fuzzy neural network. Optical Eng. 43: 103-112. 2004.
    • (2004) Optical Eng. , vol.43 , pp. 103-112
    • Mao, C.-W.1    Liu, S.-H.2    Lin, J.-S.3
  • 227
    • 0039164627 scopus 로고
    • The additive constant problem in multidimensional scaling
    • S. J. Messick and R. P. Abelson. The additive constant problem in multidimensional scaling. Psychometrika 21: 1-17. 1956.
    • (1956) Psychometrika , vol.21 , pp. 1-17
    • Messick, S.J.1    Abelson, R.P.2
  • 228
    • 1342328031 scopus 로고    scopus 로고
    • An axiomatic characterization of a fuzzy generalization of rough sets
    • J. S. Mi and W. X. Zhang. An axiomatic characterization of a fuzzy generalization of rough sets. Info. Sci. 160 (1-4): 235-249. 2004.
    • (2004) Info. Sci. , vol.160 , Issue.1-4 , pp. 235-249
    • Mi, J.S.1    Zhang, W.X.2
  • 229
    • 85005299854 scopus 로고
    • The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains
    • (Proc of AAAI-86) AAAI Press
    • R. S. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains. (Proc of AAAI-86) AAAI Press, pp. 1041-1047. 1986
    • (1986) , pp. 1041-1047
    • Michalski, R.S.1    Mozetic, I.2    Hong, J.3    Lavrac, N.4
  • 231
    • 0003971926 scopus 로고
    • Subset Selection in Regression
    • London: Chapman and Hall.
    • A. J. Miller. Subset Selection in Regression. London: Chapman and Hall. 1990.
    • (1990)
    • Miller, A.J.1
  • 232
    • 0004255908 scopus 로고    scopus 로고
    • Machine Learning
    • New York: McGraw-Hill.
    • T. Mitchell. Machine Learning. New York: McGraw-Hill. 1997.
    • (1997)
    • Mitchell, T.1
  • 233
    • 0033932032 scopus 로고    scopus 로고
    • Staging of Cervical Cancer with Soft Computing
    • P. Mitra and S. Mitra. Staging of Cervical Cancer with Soft Computing. IEEE Trans. Biomed. Eng. 47(7): 934-940. 2000.
    • (2000) IEEE Trans. Biomed. Eng. , vol.47 , Issue.7 , pp. 934-940
    • Mitra, P.1    Mitra, S.2
  • 234
    • 0033365032 scopus 로고    scopus 로고
    • Text-learning and related intelligent agents: A survey
    • D. Mladenic. Text-learning and related intelligent agents: A survey. IEEE Intell. Sys. 14(4): 44-54. 1999.
    • (1999) IEEE Intell. Sys. , vol.14 , Issue.4 , pp. 44-54
    • Mladenic, D.1
  • 237
    • 0001164225 scopus 로고    scopus 로고
    • Axiomatics for fuzzy rough sets
    • N. N. Morsi and M. M. Yakout. Axiomatics for fuzzy rough sets. Fuzzy Sets Sys. 100(1-3): 327-342. 1998.
    • (1998) Fuzzy Sets Sys. , vol.100 , Issue.1-3 , pp. 327-342
    • Morsi, N.N.1    Yakout, M.M.2
  • 238
    • 0032340171 scopus 로고    scopus 로고
    • Amalthaea: An evolving multi-agent information filtering and discovery system for the WWW
    • A. Moukas and P. Maes. Amalthaea: An evolving multi-agent information filtering and discovery system for the WWW. J. Autonomous Agents Multi-Agent Sys. 1(1): 59-88. 1998.
    • (1998) J. Autonomous Agents Multi-Agent Sys. , vol.1 , Issue.1 , pp. 59-88
    • Moukas, A.1    Maes, P.2
  • 240
    • 77951503082 scopus 로고
    • Inverse entailment and Progol
    • S. Muggleton. Inverse entailment and Progol. New Gen. Comput. 13(3-4): 245-286. 1995.
    • (1995) New Gen. Comput. , vol.13 , Issue.3-4 , pp. 245-286
    • Muggleton, S.1
  • 241
  • 243
    • 0031651436 scopus 로고    scopus 로고
    • Evolutionary algorithms for constructing linguistic rule-based systems for high-dimensional pattern classification problems
    • Piscataway, NJ: IEEE Press
    • T. Nakashima, H. Ishibuchi, and T. Murata. Evolutionary algorithms for constructing linguistic rule-based systems for high-dimensional pattern classification problems. In Proceedings of 1998 IEEE International Conference on Evolutionary Computation. Piscataway, NJ: IEEE Press, pp. 752-757. 1998.
    • (1998) Proceedings of 1998 IEEE International Conference on Evolutionary Computation. , pp. 752-757
    • Nakashima, T.1    Ishibuchi, H.2    Murata, T.3
  • 245
    • 0003909358 scopus 로고    scopus 로고
    • Foundations of Neuro-Fuzzy Systems
    • New York: Wiley.
    • D. Nauck, F. Klawonn, R. Kruse, and F. Klawonn. Foundations of Neuro-Fuzzy Systems. New York: Wiley. 1997.
    • (1997)
    • Nauck, D.1    Klawonn, F.2    Kruse, R.3    Klawonn, F.4
  • 246
    • 0001703957 scopus 로고    scopus 로고
    • A neuro-fuzzy method to learn fuzzy classification rules from data
    • D. Nauck and R. Kruse. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets Sys. 89(3): 277-288. 1997.
    • (1997) Fuzzy Sets Sys. , vol.89 , Issue.3 , pp. 277-288
    • Nauck, D.1    Kruse, R.2
  • 247
    • 0003136237 scopus 로고
    • Efficient and effective clustering methods for spatial data mining
    • San Francisco: Morgan Kaufmann
    • R. T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In Proceedings of International Conference Very Large Data Bases. San Francisco: Morgan Kaufmann, pp. 144-155. 1994.
    • (1994) Proceedings of International Conference Very Large Data Bases. , pp. 144-155
    • Ng, R.T.1    Han, J.2
  • 251
    • 33646008582 scopus 로고    scopus 로고
    • Rough set approach to sunspot classification problem
    • In D Ślȩzak et al., eds., LNAI Springer-Verlag
    • S. H. Nguyen, T. T. Nguyen, and H. S. Nguyen. Rough set approach to sunspot classification problem. In D Ślȩzak et al., eds., LNAI Springer-Verlag, pp. 263-272. 2005.
    • (2005) , pp. 263-272
    • Nguyen, S.H.1    Nguyen, T.T.2    Nguyen, H.S.3
  • 252
    • 9444229364 scopus 로고    scopus 로고
    • Rough set-based classification of EEG-signals to detect intraoperative awareness: Comparison of fuzzy and crisp discretization of real value attributes
    • In S. Tsumoto et al., eds., LNAI 3066
    • M. Ningler, G. Stockmanns, G. Schneider, O. Dressler, and E. F. Kochs. Rough set-based classification of EEG-signals to detect intraoperative awareness: Comparison of fuzzy and crisp discretization of real value attributes. In S. Tsumoto et al., eds., LNAI 3066, pp. 825-834. 2004.
    • (2004) , pp. 825-834
    • Ningler, M.1    Stockmanns, G.2    Schneider, G.3    Dressler, O.4    Kochs, E.F.5
  • 253
    • 0003858954 scopus 로고    scopus 로고
    • Discernibility and rough sets in medicine: Tools and applications
    • Department of Computer and Information Science. Norwegian University of Science and Technology, Trondheim,Norway. Report 133/1999.
    • A. Ohrn. Discernibility and rough sets in medicine: Tools and applications. Department of Computer and Information Science. Norwegian University of Science and Technology, Trondheim,Norway. Report 133/1999. 1999.
    • (1999)
    • Ohrn, A.1
  • 256
    • 85055786239 scopus 로고    scopus 로고
    • Pattern Recognition Algorithms for Data Mining
    • London: Chapman and Hall
    • S. K. Pal. Pattern Recognition Algorithms for Data Mining. London: Chapman and Hall, 2004.
    • (2004)
    • Pal, S.K.1
  • 257
    • 0003811440 scopus 로고    scopus 로고
    • Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
    • New York: Wiley.
    • S. K. Pal and S. Mitra. Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. New York: Wiley. 1999.
    • (1999)
    • Pal, S.K.1    Mitra, S.2
  • 258
    • 0003678852 scopus 로고    scopus 로고
    • Rough-Fuzzy Hybridization: A New Trend in Decision Making
    • Berlin: Springer.
    • S. K. Pal and A. Skowron, eds. Rough-Fuzzy Hybridization: A New Trend in Decision Making. Berlin: Springer. 1999.
    • (1999)
    • Pal, S.K.1    Skowron, A.2
  • 259
    • 0037252858 scopus 로고    scopus 로고
    • Rough-fuzzy MLP: Modular evolution, rule generation, and evaluation
    • S. K. Pal, S. Mitra, and P. Mitra. Rough-fuzzy MLP: Modular evolution, rule generation, and evaluation. IEEE Trans. Knowledge Data Eng. 15(1): 14-25. 2003.
    • (2003) IEEE Trans. Knowledge Data Eng. , vol.15 , Issue.1 , pp. 14-25
    • Pal, S.K.1    Mitra, S.2    Mitra, P.3
  • 261
    • 0003397496 scopus 로고
    • Rough Sets: Theoretical Aspects of Reasoning About Data
    • Dordrecht: Kluwer Academic.
    • Z Pawlak. Rough Sets: Theoretical Aspects of Reasoning About Data. Dordrecht: Kluwer Academic. 1991.
    • (1991)
    • Pawlak, Z.1
  • 262
    • 0002970340 scopus 로고
    • Rough membership functions
    • In R. Yager, M. Fedrizzi, and J. Kacprzyk, eds., New York: Wiley
    • Z. Pawlak and A. Skowron. Rough membership functions. In R. Yager, M. Fedrizzi, and J. Kacprzyk, eds., Advances in the Dempster-Shafer Theory of Evidence. New York: Wiley, pp. 251-271. 1994.
    • (1994) Advances in the Dempster-Shafer Theory of Evidence. , pp. 251-271
    • Pawlak, Z.1    Skowron, A.2
  • 263
    • 26944467801 scopus 로고    scopus 로고
    • Some Issues on rough sets
    • Z Pawlak. Some Issues on rough sets. LNCS Trans. Rough Sets (1): 1-53. 2003.
    • (2003) LNCS Trans. Rough Sets , Issue.1 , pp. 1-53
    • Pawlak, Z.1
  • 264
    • 0001454867 scopus 로고
    • On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can reasonably supposed to have arisen from random sampling
    • K Pearson. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can reasonably supposed to have arisen from random sampling. Philoso. Mag. Series 5: 157-175. 1900.
    • (1900) Philoso. Mag. Series , vol.5 , pp. 157-175
    • Pearson, K.1
  • 265
    • 0003642146 scopus 로고    scopus 로고
    • Fuzzy Modelling: Paradigms and Practice
    • Norwell, MA: Kluwer Academic Press.
    • W Pedrycz. Fuzzy Modelling: Paradigms and Practice. Norwell, MA: Kluwer Academic Press. 1996.
    • (1996)
    • Pedrycz, W.1
  • 266
    • 0008435541 scopus 로고    scopus 로고
    • Shadowed sets: Bridging fuzzy and rough sets
    • In [258]
    • W Pedrycz. Shadowed sets: Bridging fuzzy and rough sets. In [258], pp. 179-199. 1999.
    • (1999) , pp. 179-199
    • Pedrycz, W.1
  • 267
    • 0003553261 scopus 로고    scopus 로고
    • An Introduction to Fuzzy Sets: Analysis and Design
    • Cambridge: MIT Press.
    • W. Pedrycz and F. Gomide. An Introduction to Fuzzy Sets: Analysis and Design. Cambridge: MIT Press. 1998.
    • (1998)
    • Pedrycz, W.1    Gomide, F.2
  • 268
    • 0036532805 scopus 로고    scopus 로고
    • Feature analysis through information granulation
    • W. Pedrycz and G. Vukovich. Feature analysis through information granulation. Pattern Recog. 35(4): 825-834. 2002.
    • (2002) Pattern Recog. , vol.35 , Issue.4 , pp. 825-834
    • Pedrycz, W.1    Vukovich, G.2
  • 270
    • 0037332842 scopus 로고    scopus 로고
    • Classification of meteorological volumetric radar data using rough set methods
    • J. F. Peters, Z. Suraj, S. Shan, S. Ramanna, W. Pedrycz, and N. J. Pizzi. Classification of meteorological volumetric radar data using rough set methods. Pattern Recog. Lett. 24 (6): 911-920. 2003.
    • (2003) Pattern Recog. Lett. , vol.24 , Issue.6 , pp. 911-920
    • Peters, J.F.1    Suraj, Z.2    Shan, S.3    Ramanna, S.4    Pedrycz, W.5    Pizzi, N.J.6
  • 271
    • 33750927529 scopus 로고    scopus 로고
    • Unsupervised texture discrimination based on rough fuzzy sets and parallel hierarchical clustering
    • Piscataway, NJ: IEEE Press
    • A. Petrosino and M. Ceccarelli. Unsupervised texture discrimination based on rough fuzzy sets and parallel hierarchical clustering. In Proceedings of IEEE International Conference on Pattern Recognition. Piscataway, NJ: IEEE Press, pp. 1100-1103, 2000.
    • (2000) Proceedings of IEEE International Conference on Pattern Recognition. , pp. 1100-1103
    • Petrosino, A.1    Ceccarelli, M.2
  • 272
    • 0003120218 scopus 로고    scopus 로고
    • Fast Training of Support Vector Machines using Sequential Minimal Optimization
    • In B. Schlkopf, C. Burges, and A. Smola, eds. Cambridge: MIT Press
    • J. Platt. Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schlkopf, C. Burges, and A. Smola, eds., Advances in Kernel Methods: Support Vector Learning. Cambridge: MIT Press, pp. 185-208. 1998.
    • (1998) Advances in Kernel Methods: Support Vector Learning. , pp. 185-208
    • Platt, J.1
  • 273
    • 10944261672 scopus 로고    scopus 로고
    • Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems
    • Heidelberg: Physica.
    • L. Polkowski, T. Y. Lin, and S. Tsumoto, eds. Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, Vol. 56. Studies in Fuzziness and Soft Computing. Heidelberg: Physica. 2000.
    • (2000) Studies in Fuzziness and Soft Computing. , vol.56
    • Polkowski, L.1    Lin, T.Y.2    Tsumoto, S.3
  • 274
    • 10944271600 scopus 로고    scopus 로고
    • Rough Sets: Mathematical Foundations
    • Heidelberg: Physica.
    • L Polkowski. Rough Sets: Mathematical Foundations. Advances in Soft Computing. Heidelberg: Physica. 2002.
    • (2002) Advances in Soft Computing.
    • Polkowski, L.1
  • 275
    • 84873193051 scopus 로고    scopus 로고
    • Rough Clustering: An Alternative to Find Meaningful Clusters by Using the Reducts from a Dataset Source
    • Berlin: Springer
    • H. A. do Prado, P. M. Engel, and H. C. Filho. Rough Clustering: An Alternative to Find Meaningful Clusters by Using the Reducts from a Dataset Source. Berlin: Springer, pp. 234-238. 2002.
    • (2002) , pp. 234-238
    • Do Prado, H.A.1    Engel, P.M.2    Filho, H.C.3
  • 276
    • 84957618344 scopus 로고    scopus 로고
    • Rough set based data exploration using ROSE system
    • In Z. W. Ras and A. Skowron, eds., Berlin: Springer
    • B. Predki and Sz. Wilk. Rough set based data exploration using ROSE system. In Z. W. Ras and A. Skowron, eds., Foundations of Intelligent Systems. Berlin: Springer, pp. 172-180. 1999.
    • (1999) Foundations of Intelligent Systems. , pp. 172-180
    • Predki, B.1    Wilk, S.2
  • 277
    • 0001660630 scopus 로고    scopus 로고
    • An improvement to Kóczy and Hirota's interpolative reasoning in sparse fuzzy rule bases
    • W. Z. Qiao, M. Mizumoto, and S. Y. Yan. An improvement to Kóczy and Hirota's interpolative reasoning in sparse fuzzy rule bases. Int. J. Approx. Reason. 15: 185-201. 1996.
    • (1996) Int. J. Approx. Reason. , vol.15 , pp. 185-201
    • Qiao, W.Z.1    Mizumoto, M.2    Yan, S.Y.3
  • 278
    • 14544278872 scopus 로고    scopus 로고
    • On the topological properties of fuzzy rough sets
    • K. Qina and Z. Pei. On the topological properties of fuzzy rough sets. Fuzzy Sets Sys. 151(3): 601-613. 2005.
    • (2005) Fuzzy Sets Sys. , vol.151 , Issue.3 , pp. 601-613
    • Qina, K.1    Pei, Z.2
  • 279
    • 33744584654 scopus 로고
    • Induction of decision trees
    • J. R. Quinlan. Induction of decision trees. Machine Learn. 1: 81-106. 1986.
    • (1986) Machine Learn. , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 280
    • 0001172265 scopus 로고
    • Learning logical definitions from relations
    • J. R. Quinlan. Learning logical definitions from relations. Machine Learn. 5(3): 239-266. 1990.
    • (1990) Machine Learn. , vol.5 , Issue.3 , pp. 239-266
    • Quinlan, J.R.1
  • 281
    • 0003500248 scopus 로고
    • C4.5: Programs for Machine Learning
    • San Mateo, CA: Morgan Kaufmann Publishers.
    • J. R. Quinlan. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers. 1993.
    • (1993)
    • Quinlan, J.R.1
  • 282
    • 0036498107 scopus 로고    scopus 로고
    • A comparative study of fuzzy rough sets
    • A. M. Radzikowska and E. E. Kerre. A comparative study of fuzzy rough sets. Fuzzy Sets Sys. 126(2): 137-155. 2002.
    • (2002) Fuzzy Sets Sys. , vol.126 , Issue.2 , pp. 137-155
    • Radzikowska, A.M.1    Kerre, E.E.2
  • 283
    • 33646256658 scopus 로고    scopus 로고
    • Fuzzy rough sets based on residuated lattices
    • Berlin: Springer
    • A. M. Radzikowska and E. E. Kerre. Fuzzy rough sets based on residuated lattices. In Transactions on Rough Sets II. Berlin: Springer, pp. 278-296. 2004.
    • (2004) Transactions on Rough Sets II. , pp. 278-296
    • Radzikowska, A.M.1    Kerre, E.E.2
  • 285
    • 0346545485 scopus 로고    scopus 로고
    • Instance-based filter for feature selection
    • B. Raman and T.R. Ioerger. Instance-based filter for feature selection. J. Machine Learn. Res. 1: 1-23. 2002.
    • (2002) J. Machine Learn. Res. , vol.1 , pp. 1-23
    • Raman, B.1    Ioerger, T.R.2
  • 286
    • 11144280605 scopus 로고    scopus 로고
    • Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers
    • NJ: IEEE Press
    • K. Rasmani and Q. Shen. Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers. In Proceedings of 13th International Conference on Fuzzy Systems, NJ: IEEE Press, pp. 1687-1694. 2004.
    • (2004) Proceedings of 13th International Conference on Fuzzy Systems , pp. 1687-1694
    • Rasmani, K.1    Shen, Q.2
  • 287
    • 33750711719 scopus 로고    scopus 로고
    • Data-driven fuzzy rule generation and its application for student academic performance evaluation
    • K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Appl. Intell. 25(3): 305-319. 2006.
    • (2006) Appl. Intell. , vol.25 , Issue.3 , pp. 305-319
    • Rasmani, K.1    Shen, Q.2
  • 289
    • 0012672426 scopus 로고
    • Multidimensional psychophysics
    • M. W. Richardson. Multidimensional psychophysics. Psycholog. Bull. 35: 659-660. 1938.
    • (1938) Psycholog. Bull. , vol.35 , pp. 659-660
    • Richardson, M.W.1
  • 290
    • 2442585117 scopus 로고    scopus 로고
    • A genetic algorithm for discovering interesting fuzzy prediction rules: Applications to science and technology data
    • Morgan Kaufmann
    • W. Romao, A. A. Freitas, and R. C. S. Pacheco. A genetic algorithm for discovering interesting fuzzy prediction rules: Applications to science and technology data. In Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp. 343-350. 2002.
    • (2002) Proceedings of the Genetic and Evolutionary Computation Conference. , pp. 343-350
    • Romao, W.1    Freitas, A.A.2    Pacheco, R.C.S.3
  • 291
    • 84889358532 scopus 로고    scopus 로고
    • The ROSETTA homepage. Available at
    • The ROSETTA homepage. Available at http://rosetta.lcb.uu.se/general/.
  • 292
    • 84889484720 scopus 로고    scopus 로고
    • RSES: Rough Set Exploration System. Available at
    • RSES: Rough Set Exploration System. Available at http://logic.mimuw.edu.pl/~rses/.
  • 293
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500): 2323-2326. 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 294
    • 0012975713 scopus 로고
    • Turning the key
    • M. Ruggiero. Turning the key. Futures 23(14): 38-40. 1994.
    • (1994) Futures , vol.23 , Issue.14 , pp. 38-40
    • Ruggiero, M.1
  • 296
    • 0000646059 scopus 로고
    • Learning internal representations by error propagating
    • In E. Rumelhant and J. McCkekkand, eds., Cambridge: MIT Press.
    • D. Rumelhant, E. Hinton, and R. Williams. Learning internal representations by error propagating. In E. Rumelhant and J. McCkekkand, eds., Parallel Distributed Processing. Cambridge: MIT Press. 1986.
    • (1986) Parallel Distributed Processing.
    • Rumelhant, D.1    Hinton, E.2    Williams, R.3
  • 297
    • 0031076949 scopus 로고    scopus 로고
    • Selection of appropriate defuzzification methods using application specific properties
    • T. Runkler. Selection of appropriate defuzzification methods using application specific properties. IEEE Trans. Fuzzy Sys. 5(1): 72-79. 1997.
    • (1997) IEEE Trans. Fuzzy Sys. , vol.5 , Issue.1 , pp. 72-79
    • Runkler, T.1
  • 298
    • 0003584577 scopus 로고
    • Artificial Intelligence: A Modern Approach
    • Englewood Cliffs, NJ: Prentice Hall.
    • S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall. 1995.
    • (1995)
    • Russell, S.1    Norvig, P.2
  • 299
    • 2942541354 scopus 로고    scopus 로고
    • Feature selection for splice site prediction: A new method using EDA-based feature ranking
    • Y. Saeys, S. Degroeve, D. Aeyels, P. Rouze, and Y. Van De Peer. Feature selection for splice site prediction: A new method using EDA-based feature ranking. BMC Bioinform. 5(64): 2004.
    • (2004) BMC Bioinform. , vol.5 , Issue.64
    • Saeys, Y.1    Degroeve, S.2    Aeyels, D.3    Rouze, P.4    Van De Peer, Y.5
  • 300
    • 0016572913 scopus 로고
    • A vector space model for automatic indexing
    • G. Salton, A. Wong, and C.S. Yang. A vector space model for automatic indexing. Comm. ACM 18(11): 613-620. 1975.
    • (1975) Comm. ACM , vol.18 , Issue.11 , pp. 613-620
    • Salton, G.1    Wong, A.2    Yang, C.S.3
  • 301
    • 0020848262 scopus 로고
    • Extended Boolean information retrieval
    • G. Salton, E. A. Fox, and H. Wu. Extended Boolean information retrieval. Comm. ACM 26(12): 1022-1036. 1983.
    • (1983) Comm. ACM , vol.26 , Issue.12 , pp. 1022-1036
    • Salton, G.1    Fox, E.A.2    Wu, H.3
  • 302
    • 0003653039 scopus 로고
    • Introduction to Modern Information Retrieval
    • New York: McGraw-Hill.
    • G. Salton, Introduction to Modern Information Retrieval. New York: McGraw-Hill. 1983.
    • (1983)
    • Salton, G.1
  • 303
    • 0003862399 scopus 로고
    • Term weighting approaches in automatic text retrieval
    • Technical report TR87-881. Department of Computer Science, Cornell University.
    • G. Salton, and C. Buckley. Term weighting approaches in automatic text retrieval. Technical report TR87-881. Department of Computer Science, Cornell University. 1987.
    • (1987)
    • Salton, G.1    Buckley, C.2
  • 305
    • 32644434452 scopus 로고    scopus 로고
    • Ruggedness measures of medical time series using fuzzy-rough sets and fractals
    • M. Sarkar. Ruggedness measures of medical time series using fuzzy-rough sets and fractals. Pattern Recog. Lett. 27(5): 447-454. 2006.
    • (2006) Pattern Recog. Lett. , vol.27 , Issue.5 , pp. 447-454
    • Sarkar, M.1
  • 306
    • 85152626023 scopus 로고
    • Efficiently inducing determinations-A complete and systematic search algorithm that uses optimal pruning
    • Morgan Kaufmann
    • J. C. Schlimmer. Efficiently inducing determinations-A complete and systematic search algorithm that uses optimal pruning. International Conference on Machine Learning. Morgan Kaufmann, pp. 284-290. 1993.
    • (1993) International Conference on Machine Learning. , pp. 284-290
    • Schlimmer, J.C.1
  • 307
    • 0003893955 scopus 로고    scopus 로고
    • Support Vector Learning
    • Munich: Oldenbourg Verlag.
    • B. Schölkopf. Support Vector Learning. Munich: Oldenbourg Verlag. 1997.
    • (1997)
    • Schölkopf, B.1
  • 308
    • 0003932737 scopus 로고
    • Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise
    • New York: Freeman.
    • M. Schroeder. Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. New York: Freeman. 1991.
    • (1991)
    • Schroeder, M.1
  • 309
    • 0002442796 scopus 로고    scopus 로고
    • Machine learning in automated text categorisation
    • F. Sebastiani. Machine learning in automated text categorisation. ACM Comput. Sur. 34(1): 1-47. 2002.
    • (2002) ACM Comput. Sur. , vol.34 , Issue.1 , pp. 1-47
    • Sebastiani, F.1
  • 310
    • 0036532821 scopus 로고    scopus 로고
    • A hybrid filter/wrapper approach of feature selection using information theory
    • M. Sebban and R. Nock. A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recog. 35(4): 835-846. 2002.
    • (2002) Pattern Recog. , vol.35 , Issue.4 , pp. 835-846
    • Sebban, M.1    Nock, R.2
  • 312
    • 0031140388 scopus 로고    scopus 로고
    • Neural network feature selector
    • R. Setiono and H. Liu. Neural network feature selector. IEEE Trans. Neural Net. 8(3): 645-662. 1997.
    • (1997) IEEE Trans. Neural Net. , vol.8 , Issue.3 , pp. 645-662
    • Setiono, R.1    Liu, H.2
  • 313
    • 0034294243 scopus 로고    scopus 로고
    • GA-fuzzy modeling and classification: Complexity and performance
    • M. Setnes and H. Roubos. GA-fuzzy modeling and classification: Complexity and performance. IEEE Trans. Fuzzy Sys. 8(5): 509-522. 2000.
    • (2000) IEEE Trans. Fuzzy Sys. , vol.8 , Issue.5 , pp. 509-522
    • Setnes, M.1    Roubos, H.2
  • 314
    • 16244391340 scopus 로고    scopus 로고
    • The status of research on rough sets for knowledge discovery in databases
    • Cambridge: European Conference Publications
    • H. Sever. The status of research on rough sets for knowledge discovery in databases. In Proceedings of 2nd International Conference on Nonlinear Problems in Aviation and Aerospace, Cambridge: European Conference Publications, Vol. 2. pp. 673-680. 1998.
    • (1998) Proceedings of 2nd International Conference on Nonlinear Problems in Aviation and Aerospace , vol.2 , pp. 673-680
    • Sever, H.1
  • 315
    • 0004209735 scopus 로고
    • A Mathematical Theory of Evidence
    • Princeton: Princeton University Press.
    • G. Shafer. A Mathematical Theory of Evidence. Princeton: Princeton University Press. 1976.
    • (1976)
    • Shafer, G.1
  • 317
    • 10944273204 scopus 로고    scopus 로고
    • Rough feature selection for neural network based image classification
    • C. Shang and Q. Shen. Rough feature selection for neural network based image classification. Int. J. Image Graph. 2(4): 541-556. 2002.
    • (2002) Int. J. Image Graph. , vol.2 , Issue.4 , pp. 541-556
    • Shang, C.1    Shen, Q.2
  • 318
    • 33751373279 scopus 로고    scopus 로고
    • Aiding classification of gene expression data with feature selection: A comparative study
    • C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: A comparative study. Comput. Intell. Res. 1(1): 68-76. 2006.
    • (2006) Comput. Intell. Res. , vol.1 , Issue.1 , pp. 68-76
    • Shang, C.1    Shen, Q.2
  • 319
    • 38149137268 scopus 로고    scopus 로고
    • Rough feature selection for intelligent classifiers
    • Q. Shen. Rough feature selection for intelligent classifiers. LNCS Trans. Rough Sets 7: 244-255. 2007.
    • (2007) LNCS Trans. Rough Sets , vol.7 , pp. 244-255
    • Shen, Q.1
  • 320
    • 0035009319 scopus 로고    scopus 로고
    • FuREAP: A fuzzy-rough estimator of algae population
    • Q. Shen and A. Chouchoulas. FuREAP: A fuzzy-rough estimator of algae population. Art. Intell. Eng. 15(1): 13-24. 2001.
    • (2001) Art. Intell. Eng. , vol.15 , Issue.1 , pp. 13-24
    • Shen, Q.1    Chouchoulas, A.2
  • 321
    • 0036833247 scopus 로고    scopus 로고
    • A fuzzy-rough approach for generating classification rules
    • Q. Shen and A. Chouchoulas. A fuzzy-rough approach for generating classification rules. Pattern Recog. 35(11): 341-354. 2002.
    • (2002) Pattern Recog. , vol.35 , Issue.11 , pp. 341-354
    • Shen, Q.1    Chouchoulas, A.2
  • 322
    • 2442528339 scopus 로고    scopus 로고
    • Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring
    • Q. Shen and R. Jensen. Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern Recog. 37(7): 1351-1363. 2004.
    • (2004) Pattern Recog. , vol.37 , Issue.7 , pp. 1351-1363
    • Shen, Q.1    Jensen, R.2
  • 323
    • 34547436201 scopus 로고    scopus 로고
    • Rough Sets, their Extensions and Applications
    • Q. Shen and R. Jensen. Rough Sets, their Extensions and Applications. Int. J. Auto. Comput. 4(3): 217-228. 2007.
    • (2007) Int. J. Auto. Comput. , vol.4 , Issue.3 , pp. 217-228
    • Shen, Q.1    Jensen, R.2
  • 324
    • 38149127216 scopus 로고    scopus 로고
    • Approximation-based feature selection and application for algae population estimation
    • forthcoming
    • Q. Shen and R. Jensen. Approximation-based feature selection and application for algae population estimation. in Appl. Intell., forthcoming 28(2): 167-181.
    • Appl. Intell. , vol.28 , Issue.2 , pp. 167-181
    • Shen, Q.1    Jensen, R.2
  • 325
    • 0034249467 scopus 로고    scopus 로고
    • Fault diagnosis using rough sets theory
    • L. Shen, F. E. H. Tay, L. Qu, and Y. Shen. Fault diagnosis using rough sets theory. Comput. Indus. 43: 61-72. 2000.
    • (2000) Comput. Indus. , vol.43 , pp. 61-72
    • Shen, L.1    Tay, F.E.H.2    Qu, L.3    Shen, Y.4
  • 326
    • 34250926052 scopus 로고
    • The analysis of proximities: Multidimensional scaling with an unknown distance function, I, II
    • R. N. Shepard. The analysis of proximities: Multidimensional scaling with an unknown distance function, I, II. Psychometrika 27: 125-140, 219-246. 1962.
    • (1962) Psychometrika , vol.27
    • Shepard, R.N.1
  • 327
    • 0029233518 scopus 로고
    • Some considerations on Kóczy's interpolative reasoning method
    • Yokohama, Japan. Piscataway, NJ: IEEE Press
    • Y. Shi and M. Mizumoto. Some considerations on Kóczy's interpolative reasoning method. In Proceedings of FUZZ-IEEE'95 , Yokohama, Japan. Piscataway, NJ: IEEE Press, pp. 2117-2122. 1995.
    • (1995) Proceedings of FUZZ-IEEE'95 , pp. 2117-2122
    • Shi, Y.1    Mizumoto, M.2
  • 330
    • 0024895461 scopus 로고
    • A note on genetic algorithms for large-scale feature selection
    • W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recog. Lett. 10(5): 335-347. 1989.
    • (1989) Pattern Recog. Lett. , vol.10 , Issue.5 , pp. 335-347
    • Siedlecki, W.1    Sklansky, J.2
  • 331
    • 0003443397 scopus 로고
    • Density Estimation for Statistics and Data Analysis
    • London: Chapman and Hall.
    • B. W. Silverman. Density Estimation for Statistics and Data Analysis. London: Chapman and Hall. 1986.
    • (1986)
    • Silverman, B.W.1
  • 333
    • 17844376964 scopus 로고
    • The discernibility matrices and functions in information systems
    • In [340]
    • A. Skowron and C. Rauszer. The discernibility matrices and functions in information systems. In [340], pp. 331-362. 1992.
    • (1992) , pp. 331-362
    • Skowron, A.1    Rauszer, C.2
  • 335
  • 337
    • 2442711286 scopus 로고    scopus 로고
    • Rough sets, pattern recognition and data mining
    • A. Skowron, S. K. Pal. Rough sets, pattern recognition and data mining. Pattern Recog. Lett. 24(6): 829-933. 2003.
    • (2003) Pattern Recog. Lett. , vol.24 , Issue.6 , pp. 829-933
    • Skowron, A.1    Pal, S.K.2
  • 339
    • 0012569466 scopus 로고    scopus 로고
    • Normalized decision functions and measures for inconsistent decision tables analysis
    • D. Ślȩzak. Normalized decision functions and measures for inconsistent decision tables analysis. Fundamenta Informaticae 44(3): 291-319. 2000.
    • (2000) Fundamenta Informaticae , vol.44 , Issue.3 , pp. 291-319
    • Ślȩzak, D.1
  • 340
    • 0003391417 scopus 로고
    • Intelligent Decision Support
    • Dordrecht: Kluwer Academic.
    • R. Slowinski, ed. Intelligent Decision Support. Dordrecht: Kluwer Academic. 1992.
    • (1992)
    • Slowinski, R.1
  • 342
    • 0002673897 scopus 로고
    • Application of the rough set approach to evaluation of bankruptcy risk
    • R. Slowinski and C. Zopounidis. Application of the rough set approach to evaluation of bankruptcy risk. Int. J. Intell. Sys. Account. Fin. Manag. 4(1): 27-41. 1995.
    • (1995) Int. J. Intell. Sys. Account. Fin. Manag. , vol.4 , Issue.1 , pp. 27-41
    • Slowinski, R.1    Zopounidis, C.2
  • 344
    • 0013007336 scopus 로고    scopus 로고
    • Rough set predictor of business failure
    • In R. A. Ribeiro, H. J. Zimmermann, R. R. Yager, and J. Kacprzyk, eds., Wurzburg: Physica
    • R. Slowinski, C. Zopounidis, A. I. Dimitras, and R. Susmaga. Rough set predictor of business failure. In R. A. Ribeiro, H. J. Zimmermann, R. R. Yager, and J. Kacprzyk, eds. Soft Computing in Financial Engineering. Wurzburg: Physica, pp. 402-424. 1999.
    • (1999) Soft Computing in Financial Engineering. , pp. 402-424
    • Slowinski, R.1    Zopounidis, C.2    Dimitras, A.I.3    Susmaga, R.4
  • 345
    • 0000621362 scopus 로고
    • Implication in fuzzy logic
    • P. Smets and P. Magrez. Implication in fuzzy logic. Int. J. Approx. Reason. 1(4): 327-347. 1987.
    • (1987) Int. J. Approx. Reason. , vol.1 , Issue.4 , pp. 327-347
    • Smets, P.1    Magrez, P.2
  • 346
    • 0003401675 scopus 로고    scopus 로고
    • A Tutorial on Support Vector Regression
    • Neuro-COLT2 Technical Report Series.
    • A. J. Smola and B. Schölkopf. A Tutorial on Support Vector Regression. Neuro-COLT2 Technical Report Series. 1998.
    • (1998)
    • Smola, A.J.1    Schölkopf, B.2
  • 347
    • 35248885574 scopus 로고    scopus 로고
    • Feature construction and selection using genetic programming and a genetic algorithm
    • Springer
    • M. G. Smith and L. Bull. Feature construction and selection using genetic programming and a genetic algorithm. In Proceedings of 6th European Conference on Genetic Programming. Springer, pp. 229-237. 2003.
    • (2003) Proceedings of 6th European Conference on Genetic Programming. , pp. 229-237
    • Smith, M.G.1    Bull, L.2
  • 348
    • 0003588857 scopus 로고
    • A learning system based on genetic adaptive algorithms
    • PhD thesis. Computer Science Department, University of Pittsburgh.
    • S. F. Smith. A learning system based on genetic adaptive algorithms. PhD thesis. Computer Science Department, University of Pittsburgh. 1980.
    • (1980)
    • Smith, S.F.1
  • 349
    • 0035155598 scopus 로고    scopus 로고
    • Vocabulary mining for information retrieval: Rough sets and fuzzy sets
    • P. Srinivasan, M. E. Ruiz, D. H. Kraft, and J. Chen. Vocabulary mining for information retrieval: Rough sets and fuzzy sets. Info. Process. Manag. 37(1): 15-38. 1998.
    • (1998) Info. Process. Manag. , vol.37 , Issue.1 , pp. 15-38
    • Srinivasan, P.1    Ruiz, M.E.2    Kraft, D.H.3    Chen, J.4
  • 351
    • 0002865353 scopus 로고    scopus 로고
    • On rough set based approaches to induction of decision rules
    • In A. Skowron, L. Polkowski, eds., Heidelberg: Physica
    • J. Stefanowski. On rough set based approaches to induction of decision rules. In A. Skowron, L. Polkowski, eds., Rough Sets in Knowledge Discovery, Vol. 1. Heidelberg: Physica, pp. 500-529. 1998.
    • (1998) Rough Sets in Knowledge Discovery , vol.1 , pp. 500-529
    • Stefanowski, J.1
  • 353
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions
    • M. Stone. Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. B 36: 111-147. 1974.
    • (1974) J. Roy. Stat. Soc. B , vol.36 , pp. 111-147
    • Stone, M.1
  • 355
    • 0037332841 scopus 로고    scopus 로고
    • Rough set methods in feature selection and recognition
    • R. W. Swiniarski and A. Skowron. Rough set methods in feature selection and recognition. Pattern Recog. Lett. 24(6): 833-849. 2003.
    • (2003) Pattern Recog. Lett. , vol.24 , Issue.6 , pp. 833-849
    • Swiniarski, R.W.1    Skowron, A.2
  • 356
    • 0012922901 scopus 로고
    • Tapping financial databases
    • A. Szladow and D. Mills. Tapping financial databases. Bus. Credit 95(7): 8. 1993.
    • (1993) Bus. Credit , vol.95 , Issue.7 , pp. 8
    • Szladow, A.1    Mills, D.2
  • 357
    • 0037120630 scopus 로고    scopus 로고
    • Economic and financial prediction using rough sets model
    • F. E. H. Tay and L. Shen. Economic and financial prediction using rough sets model. Eur. J. Oper. Res. 141: 641-659. 2002.
    • (2002) Eur. J. Oper. Res. , vol.141 , pp. 641-659
    • Tay, F.E.H.1    Shen, L.2
  • 358
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science 290(5500): 2319-2323. 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 359
    • 0008223777 scopus 로고    scopus 로고
    • Fuzzy rough sets versus rough fuzzy sets-An interpretation and a comparative study using concepts of modal logics
    • Technical report no. CI-30/98, University of Dortmund.
    • H. Thiele. Fuzzy rough sets versus rough fuzzy sets-An interpretation and a comparative study using concepts of modal logics. Technical report no. CI-30/98, University of Dortmund. 1998.
    • (1998)
    • Thiele, H.1
  • 360
    • 84950351930 scopus 로고
    • Multidimensional Scaling
    • W. S. Torgerson. "Multidimensional Scaling." Psychometrika 17:401-419. 1952.
    • (1952) Psychometrika , vol.17 , pp. 401-419
    • Torgerson, W.S.1
  • 364
    • 33645959505 scopus 로고    scopus 로고
    • Relevant attribute discovery in high dimensional data based on rough sets and unsupervised classification: Application to leukemia gene expressions
    • In D Ślȩzak et al., eds., Proc. of RSFDGrC 2005 Springer
    • J. J. Valdés and A. J. Barton. Relevant attribute discovery in high dimensional data based on rough sets and unsupervised classification: Application to leukemia gene expressions. In D Ślȩzak et al., eds., Proc. of RSFDGrC 2005 Springer pp. 362-371. 2005.
    • (2005) , pp. 362-371
    • Valdés, J.J.1    Barton, A.J.2
  • 365
    • 0004217877 scopus 로고
    • Information Retrieval
    • London: Butterworths., Available at
    • C. J. van Rijsbergen. Information Retrieval. London: Butterworths. 1979. Available at http://www.dcs.gla.ac.uk/Keith/Preface.html.
    • (1979)
    • Van Rijsbergen, C.J.1
  • 368
    • 3042753607 scopus 로고    scopus 로고
    • Cluster Analysis of Marketing Data: A Comparison of K-Means, Rough Set, and Rough Genetic Approaches
    • C.S. Newton, eds., edited by H.A. Abbas, R.A. Sarker, PA,Idea Group Publishing
    • K. E. Voges, N. K. L. Pope, and M. R. Brown. Cluster Analysis of Marketing Data: A Comparison of K-Means, Rough Set, and Rough Genetic Approaches. In C.S. Newton, eds., Heuristics and Optimization for Knowledge Discovery, edited by H.A. Abbas, R.A. Sarker, PA,Idea Group Publishing, pp. 208-216, 2002.
    • (2002) Heuristics and Optimization for Knowledge Discovery , pp. 208-216
    • Voges, K.E.1    Pope, N.K.L.2    Brown, M.R.3
  • 369
    • 27744505065 scopus 로고    scopus 로고
    • Computationally efficient sup-t transitive closure for sparse fuzzy binary relations
    • M. Wallace, Y. Avrithis, and S. Kollias. Computationally efficient sup-t transitive closure for sparse fuzzy binary relations. Fuzzy Sets Sys. 157(3): 341-372, 2006.
    • (2006) Fuzzy Sets Sys. , vol.157 , Issue.3 , pp. 341-372
    • Wallace, M.1    Avrithis, Y.2    Kollias, S.3
  • 372
    • 0004236801 scopus 로고
    • Kernel Smoothing
    • London: Chapman and Hall.
    • M. Wand and M. Jones. Kernel Smoothing. London: Chapman and Hall. 1995.
    • (1995)
    • Wand, M.1    Jones, M.2
  • 373
    • 0026943536 scopus 로고
    • Generating fuzzy rules by learning from examples
    • L. X. Wang and J. M. Mendel. Generating fuzzy rules by learning from examples. IEEE Trans. Sys. Man Cyber. 22(6): 1414-1427. 1992.
    • (1992) IEEE Trans. Sys. Man Cyber. , vol.22 , Issue.6 , pp. 1414-1427
    • Wang, L.X.1    Mendel, J.M.2
  • 374
    • 0035505464 scopus 로고    scopus 로고
    • Reduction Algorithms based on discernibility matrix: The ordered attributes method
    • J. Wang and J. Wang. Reduction Algorithms based on discernibility matrix: The ordered attributes method. J. Comput. Sci. Technol. 16(6): 489-504. 2001.
    • (2001) J. Comput. Sci. Technol. , vol.16 , Issue.6 , pp. 489-504
    • Wang, J.1    Wang, J.2
  • 376
    • 27544480397 scopus 로고    scopus 로고
    • A new approach to fitting linear models in high dimensional spaces
    • PhD thesis. Department of Computer Science, University of Waikato.
    • Y. Wang. A new approach to fitting linear models in high dimensional spaces. PhD thesis. Department of Computer Science, University of Waikato. 2000.
    • (2000)
    • Wang, Y.1
  • 377
    • 26944457693 scopus 로고    scopus 로고
    • Fuzzy-rough set based nearest neighbor clustering classification algorithm
    • Proc. of FSKD 2005 Berlin: Springer
    • X. Wang, J. Yang, X. Teng, and N. Peng. Fuzzy-rough set based nearest neighbor clustering classification algorithm. Proc. of FSKD 2005 Berlin: Springer, pp. 370-373, 2005.
    • (2005) , pp. 370-373
    • Wang, X.1    Yang, J.2    Teng, X.3    Peng, N.4
  • 379
    • 33646015367 scopus 로고    scopus 로고
    • Integration of Variable Precision Rough Set and Fuzzy Clustering: An Application to Knowledge Acquisition for Manufacturing Process Planning
    • Springer, RSFDGrC 2005
    • Z. Wang, X. Shao, G. Zhang, and H. Zhu. Integration of Variable Precision Rough Set and Fuzzy Clustering: An Application to Knowledge Acquisition for Manufacturing Process Planning. In Proceedings of 10th International Conference. Springer, RSFDGrC 2005, pp. 585-593. 2005.
    • (2005) Proceedings of 10th International Conference. , pp. 585-593
    • Wang, Z.1    Shao, X.2    Zhang, G.3    Zhu, H.4
  • 380
    • 0035892558 scopus 로고    scopus 로고
    • An overview of evolutionary algorithms: practical issues and common pitfalls
    • D. Whitley. An overview of evolutionary algorithms: practical issues and common pitfalls. Info. Software Technol. 43(14): 817-831. 2001.
    • (2001) Info. Software Technol. , vol.43 , Issue.14 , pp. 817-831
    • Whitley, D.1
  • 382
    • 0003957032 scopus 로고    scopus 로고
    • Data Mining: Practical Machine Learning Tools with Java Implementations
    • San Francisco: Morgan Kaufmann
    • I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools with Java Implementations. San Francisco: Morgan Kaufmann, 2000.
    • (2000)
    • Witten, I.H.1    Frank, E.2
  • 383
    • 33749684314 scopus 로고    scopus 로고
    • Analogy-based reasoning in classifier construction
    • A. Wojna. Analogy-based reasoning in classifier construction. Trans. Rough Sets, 4: 277-374, 2005.
    • (2005) Trans. Rough Sets , vol.4 , pp. 277-374
    • Wojna, A.1
  • 385
    • 0037403099 scopus 로고    scopus 로고
    • Generalized fuzzy rough sets
    • W. Z. Wu, J. S. Mi, and W. X. Zhang. Generalized fuzzy rough sets. Info. Sci. 151: 263-282, 2003.
    • (2003) Info. Sci. , vol.151 , pp. 263-282
    • Wu, W.Z.1    Mi, J.S.2    Zhang, W.X.3
  • 386
    • 1642525198 scopus 로고    scopus 로고
    • Constructive and axiomatic approaches of fuzzy approximation operators
    • W. Z.Wu andW. X. Zhang. Constructive and axiomatic approaches of fuzzy approximation operators. Info. Sci. 159(3-4): 233-254, 2004.
    • (2004) Info. Sci. , vol.159 , Issue.3-4 , pp. 233-254
    • Wu, W.Z.1    Zhang, W.X.2
  • 387
    • 26944446561 scopus 로고    scopus 로고
    • A study on relationship between fuzzy rough approximation operators and fuzzy topological spaces
    • In L. Wang and Y. Jin, eds., FSKD 2005, Berlin: Springer
    • W. Z. Wu. A study on relationship between fuzzy rough approximation operators and fuzzy topological spaces. In L. Wang and Y. Jin, eds., FSKD 2005, Berlin: Springer, pp. 167-174, 2005.
    • (2005) , pp. 167-174
    • Wu, W.Z.1
  • 388
    • 19044390948 scopus 로고    scopus 로고
    • On characterizations of (I ,T)-fuzzy rough approximation operators
    • W. Z. Wu, Y. Leung, and J. S. Mi. On characterizations of (I ,T)-fuzzy rough approximation operators. Fuzzy Sets Sys. 154(1): 76-102, 2005.
    • (2005) Fuzzy Sets Sys. , vol.154 , Issue.1 , pp. 76-102
    • Wu, W.Z.1    Leung, Y.2    Mi, J.S.3
  • 389
    • 0012320181 scopus 로고
    • Rough sets and fuzzy sets-Some remarks on interrelations
    • M. Wygralak. Rough sets and fuzzy sets-Some remarks on interrelations. Fuzzy Sets Sys. 29(2): 241-243. 1989.
    • (1989) Fuzzy Sets Sys. , vol.29 , Issue.2 , pp. 241-243
    • Wygralak, M.1
  • 390
    • 17044407257 scopus 로고    scopus 로고
    • Feature Selection in Microarray Analysis: A Practical Approach to Microarray Data Analysis
    • Dordrecht: Kluwer Academics.
    • E. P. Xing. Feature Selection in Microarray Analysis: A Practical Approach to Microarray Data Analysis. Dordrecht: Kluwer Academics. 2003.
    • (2003)
    • Xing, E.P.1
  • 391
    • 0034894874 scopus 로고    scopus 로고
    • Feature (gene) selection in gene expression-based tumor classification
    • M. Xiong, W. Li, J. Zhao, L. Jin, and E. Boerwinkle. Feature (gene) selection in gene expression-based tumor classification. Mol. Genet. Metabol. 73(3): 239-247. 2001.
    • (2001) Mol. Genet. Metabol. , vol.73 , Issue.3 , pp. 239-247
    • Xiong, M.1    Li, W.2    Zhao, J.3    Jin, L.4    Boerwinkle, E.5
  • 392
    • 0036888742 scopus 로고    scopus 로고
    • Reduction of fuzzy control rules by means of premise learning - method and case study
    • N. Xiong and L. Litz. Reduction of fuzzy control rules by means of premise learning - method and case study. Fuzzy Sets Sys. 132(2): 217-231. 2002.
    • (2002) Fuzzy Sets Sys. , vol.132 , Issue.2 , pp. 217-231
    • Xiong, N.1    Litz, L.2
  • 393
    • 84889349517 scopus 로고    scopus 로고
    • Yahoo.www.yahoo.com.
  • 394
    • 0013387890 scopus 로고
    • Reasoning conditions on Kóczy's interpolative reasoning method in sparse fuzzy rule bases
    • S. Yan, M. Mizumoto, and W. Z. Qiao. Reasoning conditions on Kóczy's interpolative reasoning method in sparse fuzzy rule bases. Fuzzy Sets Sys. 75: 63-71. 1995.
    • (1995) Fuzzy Sets Sys. , vol.75 , pp. 63-71
    • Yan, S.1    Mizumoto, M.2    Qiao, W.Z.3
  • 396
    • 0032028297 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • J. Yang and V. Honavar. Feature subset selection using a genetic algorithm. IEEE Intell. Sys. 13(1): 44-49. 1998.
    • (1998) IEEE Intell. Sys. , vol.13 , Issue.1 , pp. 44-49
    • Yang, J.1    Honavar, V.2
  • 398
    • 84868092844 scopus 로고    scopus 로고
    • Feature selection for fluoresence image classification
    • KDD Lab Proposal. Carregie Mellan University
    • J. Yao. Feature selection for fluoresence image classification. KDD Lab Proposal. Carregie Mellan University, 2001.
    • (2001)
    • Yao, J.1
  • 399
    • 84889327036 scopus 로고    scopus 로고
    • Average computation time of evolutionary algorithms for combinatorial optimisation problems
    • Final IGR Report for EPSRC grant GR/R52541/01, School of Computer Science, University of Birmingham,UK.
    • X. Yao. Average computation time of evolutionary algorithms for combinatorial optimisation problems. Final IGR Report for EPSRC grant GR/R52541/01. School of Computer Science, University of Birmingham,UK. 2003.
    • (2003)
    • Yao, X.1
  • 400
    • 0012821345 scopus 로고    scopus 로고
    • Combination of rough and fuzzy sets based on α-level sets
    • In T. Y. Lin, N. Cereone, eds., Dordrecht: Kluwer Academic
    • Y. Y. Yao. Combination of rough and fuzzy sets based on α-level sets. In T. Y. Lin, N. Cereone, eds., Rough Sets and Data Mining: Analysis of Imprecise Data. Dordrecht: Kluwer Academic, pp. 301-321. 1997.
    • (1997) , pp. 301-321
    • Yao, Y.Y.1
  • 401
    • 0032142371 scopus 로고    scopus 로고
    • A Comparative Study of Fuzzy Sets and Rough Sets
    • Y. Y. Yao. A Comparative Study of Fuzzy Sets and Rough Sets. Info. Sci. 109(1-4): 21-47, 1998.
    • (1998) Info. Sci. , vol.109 , Issue.1-4 , pp. 21-47
    • Yao, Y.Y.1
  • 404
    • 0006029208 scopus 로고
    • Theory and Applications of Multidimensional Scaling
    • Hillsdale, NJ: Eribaum Associates.
    • F. W. Young and R. M. Hamer. Theory and Applications of Multidimensional Scaling. Hillsdale, NJ: Eribaum Associates. 1994.
    • (1994)
    • Young, F.W.1    Hamer, R.M.2
  • 405
    • 0036132565 scopus 로고    scopus 로고
    • Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery
    • S. Yu, S. De Backer, and P. Scheunders. Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery. Pattern Recog. Lett. 23(1-3): 183-190. 2002.
    • (2002) Pattern Recog. Lett. , vol.23 , Issue.1-3 , pp. 183-190
    • Yu, S.1    De Backer, S.2    Scheunders, P.3
  • 406
    • 0000868331 scopus 로고
    • Induction of fuzzy decision trees
    • Y. Yuan and M. J. Shaw. Induction of fuzzy decision trees. Fuzzy Sets Sys. 69(2): 125-139. 1995.
    • (1995) Fuzzy Sets Sys. , vol.69 , Issue.2 , pp. 125-139
    • Yuan, Y.1    Shaw, M.J.2
  • 407
    • 0030283584 scopus 로고    scopus 로고
    • A genetic algorithm for generating fuzzy classification rules
    • Y. F. Yuan and H. Zhuang. A genetic algorithm for generating fuzzy classification rules. Fuzzy Sets Sys. 84(1): 1-19. 1996.
    • (1996) Fuzzy Sets Sys. , vol.84 , Issue.1 , pp. 1-19
    • Yuan, Y.F.1    Zhuang, H.2
  • 408
    • 34248666540 scopus 로고
    • Fuzzy sets
    • L. A. Zadeh. Fuzzy sets. Info. Control 8: 338-353. 1965.
    • (1965) Info. Control , vol.8 , pp. 338-353
    • Zadeh, L.A.1
  • 409
    • 0016458950 scopus 로고
    • The concept of a linguistic variable and its application to approximate reasoning
    • L. A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Info. Sci. 8: pp. 199-249, 301-357; 9: 43-80. 1975.
    • (1975) Info. Sci. , vol.8
    • Zadeh, L.A.1
  • 410
    • 49049122961 scopus 로고
    • A computational approach to fuzzy quantifiers in natural languages
    • L. A. Zadeh. A computational approach to fuzzy quantifiers in natural languages. Comput. Math. Appl. 9: 149-184. 1983.
    • (1983) Comput. Math. Appl. , vol.9 , pp. 149-184
    • Zadeh, L.A.1
  • 412
    • 0023999614 scopus 로고
    • Fuzzy logic
    • L. A. Zadeh. Fuzzy logic. IEEE Comput. 21(4): 83-92. 1988.
    • (1988) IEEE Comput. , vol.21 , Issue.4 , pp. 83-92
    • Zadeh, L.A.1
  • 415
    • 4544306553 scopus 로고    scopus 로고
    • A rough sets based approach to feature selection
    • Banff, Canada, June 27-30. IEEE Press
    • M. Zhang and J. T. Yao. A rough sets based approach to feature selection. In Proceedings of 23rd International Conference of NAFIPS, Banff, Canada, June 27-30. IEEE Press, pp. 434-439. 2004.
    • (2004) Proceedings of 23rd International Conference of NAFIPS , pp. 434-439
    • Zhang, M.1    Yao, J.T.2
  • 417
    • 0035416447 scopus 로고    scopus 로고
    • Using rough sets with heuristics for feature selection
    • N. Zhong, J. Dong, and S. Ohsuga. Using rough sets with heuristics for feature selection. J. Intell. Info. Sys. 16(3): 199-214. 2001.
    • (2001) J. Intell. Info. Sys. , vol.16 , Issue.3 , pp. 199-214
    • Zhong, N.1    Dong, J.2    Ohsuga, S.3
  • 419
    • 0027543613 scopus 로고
    • Variable precision rough set model
    • W. Ziarko. Variable precision rough set model. J. Comput. Sys. Sci. 46(1): 39-59. 1993.
    • (1993) J. Comput. Sys. Sci. , vol.46 , Issue.1 , pp. 39-59
    • Ziarko, W.1
  • 420
    • 0012923406 scopus 로고
    • An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data
    • ACM
    • W. Ziarko, R. Golan, and D. Edwards. An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data. In Proceedings of AAAI Workshop on Knowledge Discovery in Databases. ACM, pp. 89-101. 1993.
    • (1993) Proceedings of AAAI Workshop on Knowledge Discovery in Databases. , pp. 89-101
    • Ziarko, W.1    Golan, R.2    Edwards, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.