On rough sets, their recent extensions and applications

Knowledge Engineering Review

Volumn 25, Issue 4, 2010, Pages 365-395

  Mac Parthalain N ^a   Shen, Q ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION DOMAINS; PROBLEM DOMAIN; REAL-WORLD PROBLEM; ROUGH SET;

PATTERN RECOGNITION;

ROUGH SET THEORY;

EID: 79952943396     PISSN: 02698889     EISSN: 14698005     Source Type: Journal    
DOI: 10.1017/S0269888910000263     Document Type: Article

References (161)

1. American College of Radiology. 1998. Illustrated Breast Imaging Reporting and Data System BIRADS, 3rd edn. American College of Radiology.
2. Asharaf, A. & Murty, M. N. 2004. An adaptive rough fuzzy single pass algorithm for clustering large data sets. Pattern Recognition 36(12), 3015-3018.
3. Baldwin, J. F., Lawry, J. & Martin, T. P. 1997. A mass assignment based ID3 algorithm for decision tree induction. International Journal of Intelligent Systems 12(7), 523-552. (Pubitemid 127608081)
4. Bao, Z., Han, B. & Wu, S. 2006. A novel clustering algorithm based on variable precision rough-fuzzy sets. In Proceedings of the International Conference on Intelligent Computing (ICIC 2006). Kunming, China, August 16-19, 284-289. (Pubitemid 44532265)
5. Bazan, J., Nguyen, H. S., Nguyen, S. H., Synak, P. & Wroblewski, J. 2000. Rough set algorithms in classification problem. In Rough Set Methods and Applications, Polkowski, L., Tsumoto, S. & Lin, T. Y. (eds). Physica-Verlag, 49-88.
6. Bell, D. A. & Guan, J. W. 1998. Computational methods for rough classification and discovery. Journal of the American Society for Information Science 5, 403-414.
7. Beynon, M. J. 2000. An investigation of β-reduct selection within the variable precision rough sets model. In Proceedings of the Second International Conference on Rough Sets and Current Trends in Computing (RSCTC 2000), Banff, Canada, 114-122.
8. Beynon, M. J. 2001. Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134(3), 592-605.
9. Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press.
10. Bhatt, R. B. & Gopal, M. 2004. FRID: fuzzy-rough interactive dichotomizers. In Proceeding of the 2004 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'04), Budapest, 1337-1342. (Pubitemid 40027891)
11. Bian, H. & Mazlack, L. 2003. Fuzzy-rough nearest-neighbor classification approach. In Proceeding of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), Chicago, USA, 500-505.
12. Boixader, D., Jacas, J. & Recasens, J. 2000. Upper and lower approximations of fuzzy sets. International Journal of General Systems 29(4), 555-568. (Pubitemid 32031311)
13. Brassard, G. & Bratley, P. 1996. Fundamentals of Algorithms. Prentice Hall.
14. Browne, C., Düntsch, I. & Gediga, G. 1998. IRIS revisited, a comparison of discriminant enhanced rough set data analysis. In Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Polkowski, L & Skowron, A. (eds). Physica-Verlag, 347-370.
15. Chen, D., Zhang, W. X., Yeung, D. & Tsang, E. C. C. 2006. Rough approximations on a complete completely distributive lattice with applications to generalized rough sets. Information Sciences 176(13), 1829-1848.
16. Chimphlee, S., Salim, N., Ngadiman, M. S. B., Chimphlee, W. & Srinoy, S. 2006. Independent component analysis and rough fuzzy based approach to web usage mining. In Proceedings of the 24th IASTED International Conference on Artificial Intelligence and Applications, Deved, V, (ed.). International Association of Science and Technology for Development, 422-427. ACTA Press.
17. Chimphlee, W., Abdullah, A. H., Sap, M. N. M., Srinoy, S. & Chimphlee, S. 2006a. Anomaly-based intrusion detection using fuzzy rough clustering. International Conference on Hybrid Information Technology (ICHIT'06) 1, 329-334. (Pubitemid 46604098)
18. Chouchoulas, A. & Shen, Q. 2001. Rough set-aided keyword reduction for text categorisation. Applied Artificial Intelligence 15(9), 843-873. (Pubitemid 33701177)
19. Cordon, O., Gomide, F., Herrera, F., Hoffmann, F. & Magdalena, L. 2001. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141, 5-31.
20. Cornelis, C., De Cock, M. & Radzikowska, A. 2007. Vaguely quantified rough sets. In Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC2007), Lecture Notes in Artificial Intelligence 4482, 87-94.
21. Cornelis, C. & Jensen, R. 2008. A noise-tolerant approach to fuzzy-rough feature selection. In Proceedings of the 17th International Conference on Fuzzy Systems (FUZZ-IEEE'08), Hong Kong, China, 1598-1605.
22. Dash, M. & Liu, H. 1997. Feature selection for classification. Intelligent Data Analysis 1(3), 131-156.
23. Davis, M., Logemann, G. & Loveland, D. 1962. A machine program for theorem proving. Communications of the ACM 5, 394-397.
24. De Cock, M., Cornelis, C. & Kerre, E. E. 2004. Fuzzy rough sets: beyond the obvious. IEEE International Conference on Fuzzy Systems 1, 103-108. (Pubitemid 40028087)
25. Deogun, J. S., Raghavan, V. V. & Sever, H. 1994. Rough set based classification methods and extended decision tables. In Proceedings of the International Workshop on Rough Sets and Soft Computing. San Jose, California, 302-309.
26. Deogun, J. S., Raghavan, V. V. & Sever, H. 1995. Exploiting upper approximations in the rough set methodology. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining. Quebec, Canada, 1-10.
27. Devijver, P. & Kittler, J. 1982. Pattern Recognition: A Statistical Approach. Prentice Hall.
28. Dubois, D. & Prade, H. 1990. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 191-209.
29. Dubois, D. & Prade, H. 1992. Putting rough sets and fuzzy sets together. In Intelligent Decision Support: Handbook of Applications and Advances of the Sets Theory, Slowinski, R. (ed.). Kluwer, 203-232.
30. Dunn, J. C. 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 32-57.
31. Glymin, M. & Ziarko, W. 2007. Rough set approach to spam filter learning. In Proceedings of Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP'07), Lecture Notes in Artificial Intelligence 4585, 350-359.
32. Greco, S., Matarazzo, B. & Slowiński, R. 2001. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129(1), 1-47. (Pubitemid 32071221)
33. Grzymala-Busse, D. M. & Grzymala-Busse, J. W. 1995. The usefulness of machine learning approach to knowledge acquisition. Computational Intelligence 11, 268-279.
34. Grzymala-Busse, J. W. & Wang, C. P. B. 1996. Classification methods in rule induction. In Proceedings of the 5th Intelligent Information Systems Workshop, Dȩblin, Poland, 120-126.
35. Grzymala-Busse, J. W. 2003. A comparison of three strategies to rule induction from data with numerical attributes. In Proceedings of the International Workshop on Rough Sets in Knowledge Discovery (RSKD 2003), Warsaw, Poland, 132-140.
36. Grzymala-Busse, J. W. 2006. Rough set theory with applications to data mining. In Real World Applications of Computational Intelligence, Studies in Fuzziness and Soft Computing Series, Negoita, M. & Reusch, B. (eds). Springer, Heidelberg, 223-244.
37. Han, J., Hu, X. & Lin, T. Y. 2005. Feature subset selection based on relative dependency between Attributes. In Rough Sets and Current Trends in Computing: 4th International Conference (RSCTC 2004). Uppsala, Sweden, June 1-5, 176-185.
38. Heath, M., Bowyer, K., Kopans, D., Moore, R. & Kegelmeyer, P. J. 2000. The digital database for screening mammography. In Proceedings of the International Workshop on Digital Mammography, Madison, Wiscowsow, USA, 212-218.
39. Hirano, S. & Tsumoto, S. 2000. Rough clustering and its application to medicine. Journal of Information Sciences 124, 125-137.
40. Hirano, S. & Tsumoto, S. 2003. Indiscernibility-based clustering: rough clustering. In International Fuzzy Systems Association World Congress, Lecture Notes in Computer Science 2715, 378-386. Springer-Verlag. (Pubitemid 36892419)
41. Ho, B. & Nguyen, N. B. 2002. Nonhierarchical document clustering based on a tolerance rough set model. International Journal of Intelligent Systems 17(2), 199-212. (Pubitemid 34167417)
42. Ho, T. B., Kawasaki, S. & Nguyen, N. B. 2006. Documents clustering using tolerance rough set model and its application to information retrieval. In Studies In Fuzziness and Soft Computing, Intelligent Exploration of the Web, Szczepaniak, P. S., Segovia, J., Karprzyk, J., & Zadeh, L. A. (eds). Physica-Verlag, Heidelberg, 181-196.
43. Hong, T. P., Liou, Y. L. & Wang, S. L. 2006. Learning with hierarchical quantitative attributes by fuzzy rough sets. In Proceedings of the 2006 Joint Conference on Information Sciences, Advances in Intelligent Systems Research, Taiwan, ROC, 1309-1312.
44. Hu, Q.-H. & Yu, D.-R. 2004. Variable precision dominance based rough set model and reduction algorithm for preference-ordered data. Proceedings of the 2004 International Conference on Machine Learning and Cybernetics 4, 2279-2284.
45. Hu, Q.-H. & Yu, D.-R. 2005. Fuzzy rough C-means clustering. In World Congress on Fuzzy Logic, Soft Computing, Computational Intelligence: Theories and Applications (IFSA2005), Springer Lecture Notes, Tsinghua, Beijing.
46. Hu, Q., Yu, D. & Xie, Z. 2006. Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters 27(5), 414-423. (Pubitemid 43242919)
47. Hu, Q., Zhao, H., Xie, Z. & Yu, D. 2007a. Consistency based attribute reduction. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining 2007, Zhou, Z., Li, H. & Yang, Q. (eds). Lecture Notes in Computer Science 4426, 96-107.
48. Hu, Q., Xie, Z. & Yu, D. 2007b. Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recognition 40, 3509-3521. (Pubitemid 47223272)
49. Inuiguchi, M. & Tsurumi, M. 2006. Measures based on upper approximations of rough sets for analysis of attribute importance and interaction. International Journal of Innovative Computing Information and Control 2(1), 1-12.
50. Jelonek, J., Krawiec, K., Slowiński, R., Stefanowski, J. & Szymas, J. 1994. Rough sets as an intelligent front-end for the neural network. In Proceedings of the First National Conference on Neural Networks Their Applications 2, Poland, 116-122.
51. Jensen, R. & Shen, Q. 2004a. Semantics-preserving dimensionality reduction: rough and fuzzy-rough based approaches. IEEE Transactions on Knowledge and Data Engineering 16(12), 1457-1471. (Pubitemid 40010921)
52. Jensen, R. & Shen, Q. 2004b. Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets and Systems 141(3), 469-485.
53. Jensen, R. & Shen, Q. 2005. Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets and Systems 149(1), 5-20.
54. Jensen, R. & Shen, Q. 2007. Fuzzy-rough sets assisted attribute selection. IEEE Transactions on Fuzzy Systems 15(1), 73-89. (Pubitemid 46444305)
55. Jensen, R. & Cornelis, C. 2008. A new approach to fuzzy-rough nearest neighbour classification. In Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, Akron, Ohio, USA, 310-319.
56. Jensen, R. & Shen, Q. 2008. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. IEEE Press and Wiley & Sons.
57. Jensen, R. & Shen, Q. 2009. New approaches to fuzzy-rough feature selection. IEEE Transactions on Fuzzy Systems 17(4), 824-838.
58. Jian, L.-R. & Li, M.-Y. 2007. An extension of VPRS model based on dominance relation. Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) 3, 113-118. (Pubitemid 351710872)
59. Kawasaki, S., Nguyen, N. B. & Ho, T. B. 2000. Hierarchical document clustering based on tolerance rough set model. In Principles of Data Mining and Knowledge Discovery, 4th European Conference (PKDD 2000), Lyon, France (September 13-16, 2000), Zighed, D. A., Komorowski, H. J. & Zytkow, J. M. (eds). Lecture Notes in Computer Science 1910, 13-27. Springer.
60. Ke, L., Feng, Z. & Ren, Z. 2008. An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognition Letters 29, 1351-1357.
61. Keller, J. M., Gray, M. R. & Givens, J. A. 1985. A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems Man and Cybernetics 15(4), 580-585.
62. Kim, D. & Bang, S.-Y. 2000. A handwritten numeral character classification using tolerant rough set. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(9), 923-937.
63. Komorowski, J., Pawlak, Z., Polkowski, L. & Skowron, A. 1999. Rough sets: a tutorial. In Rough-Fuzzy Hybridization: A New Trend in Decision Making, Pal, S. K. & Skowron, A. (eds). Springer-Verlag, 3-98.
64. Kotłowski, W., Dembczynski, K., Greco, S. & Słowinski, R. 2008. Stochastic dominance-based rough set model for ordinal classification. International Journal of Information Sciences 178(21), 4019-4037.
65. Kryszkiewicz, M. 1994. Maintenance of Reducts in the Variable Precision Rough Sets Model. ICS Research Report 31/94, Warsaw University of Technology.
66. Kumar, P., Krishna, P. R., Bapi, R. S. & De, S. K. 2007. Rough clustering of sequential data. Data & Knowledge Engineering 63(2), 183-199. (Pubitemid 47053939)
67. Li, R. & Wang, Z.-O. 2004. Mining classification rules using rough sets and neural networks. European Journal of Operational Research 157, 439-448.
68. Li, M., Wu, C., Zhang, Y. & Yue, Y. 2006. An improved BP network classifier based on VPRS feature reduction. The Sixth World Congress on Intelligent Control and Automation (WCICA 2006) 2, 9677-9680.
69. Lingras, P. 1996. Rough neural networks. Proceedings of the Sixth International Conference on Information Processing Management of Uncertainty in Knowledge-based Systems (IPMU'96) 2, 1445-1450.
70. Lingras, P. 1997. Comparison of neofuzzy rough neural networks. In Proceedings of the Fifth International Workshop on Rough Sets Soft Computing (RSSC'97), 259-262.
71. Lingras, P. & Davies, C. 2001. Applications of rough genetic algorithms. Computational Intelligence 17(3), 435-445. (Pubitemid 32934015)
72. Lingras, P. & West, C. 2004. Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems 23(1), 5-16.
73. Lingras, P., Hogo, M. & Snorek, M. 2004. Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets. Web Intelligence and Agent Systems 2(3), 217-230.
74. Mak, B. & Munakata, T. 2002. Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3. European Journal of Operational Research 136, 212-229. (Pubitemid 33018813)
75. Malyszko, D. & Stepaniuk, J. 2008. Standard and fuzzy rough entropy clustering algorithms in image segmentation. Rough Sets and Current Trends in Computing 5306, 409-418.
76. MacParthaláin, N., Shen, Q. & Jensen, R. 2010. A distance measure approach to exploring the rough set boundary region for attribute reduction. IEEE Transactions on Knowledge and Data Engineering 22(3), 306-317.
77. MacParthalain, N., Jensen, R., Shen, Q. & Zwiggelaar, R. 2010. Rough and fuzzy-rough methods for mammographic data analysis. Intelligent Data Analysis-An International Journal 14(2), 225-244.
78. MacParthalain, N. & Shen, Q. 2009. Exploring the boundary region of tolerance rough sets for feature selection. Pattern Recognition 42(5), 655-667.
79. McKee, T. & Lensberg, T. 2002. Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research 140(2), 436-451. (Pubitemid 34065108)
80. Mi, J. S. & Zhang, W. X. 2004. An axiomatic characterization of a fuzzy generalization of rough sets. Information Sciences 160 (1-4), 235-249.
81. Mieszkowicz-Rolka, A. & Rolka, L. 2004. Fuzzy implication operators in variable precision fuzzy rough sets model. Lecture Notes in Computer Science (LNCS) 3070, Springer, Heidelberg, 498-503. (Pubitemid 38835870)
82. Mitra, S. & Banerjee, M. 1996. Knowledge based neural net with rough sets. In Methodologies for the Conception, Design, Application of Intelligent Systems, Proceedings of the Fourth International Conference on Soft Computing (IIZUKA'96), Yamakawa, T. & Matsumoto, G. (eds). World Scientific, 213-216.
83. Mitra, P. & Mitra, S. 2000. Staging of cervical cancer with soft computing. IEEE Transactions on Biomedical Engineering 47(7), 934-940. (Pubitemid 30421829)
84. Modrzejewski, M. 1993. Feature selection using rough sets theory. In Proceedings of the 11th International Conference on Machine Learning, New Brunswick, NJ, USA, 213-226.
85. Molina, L. C., Belanche, L. & Nebot, A. 2002. Feature selection algorithms: a survey and experimental evaluation. In Proceedings of ICDM02, Maebashi City, Japan, 306-313.
86. Momin, B. F., Mitra, S. & Gupta, R. D. 2006. Reduct generation and classification of gene expression data. Proceedings of the 2006 International Conference on Hybrid information Technology (ICHIT06) 1, 699-708. (Pubitemid 46604157)
87. Morsi, N. N. & Yakout, M. M. 1998. Axiomatics for fuzzy rough sets. Fuzzy Sets and Systems 100 (1-3), 327-342. (Pubitemid 128654413)
88. Narendra, P. & Fukunaga, K. 1977. A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers C-26 (9), 917-922. (Pubitemid 8550870)
89. Ngo, C. L. & Nguyen, H. S. 2004. A tolerance rough set approach to clustering web search results. In Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (Pisa, Italy, September 20-24, 2004), Boulicaut, J., Esposito, F., Giannotti, F. & Pedreschi, D. (eds). Lecture Notes in Computer Science 3202, Springer-Verlag New York, New York, 515-517. (Pubitemid 39740434)
90. Nguyen, S. H. & Skowron, A. 1997a. Searching for relational patterns in data. In Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, Trondheim, Norway, 265-276. (Pubitemid 127097511)
91. Nguyen, S. H. & Slezak, D. 2004. Approximate reducts and association rules correspondence and complexity results. Lecture Notes in Computer Science (LNCS), Zhong, N., Skowron, A., & Ohsuga, S. (eds). 1711, Springer, Heidelberg, 137-145.
92. Øhrn, A. 1999. Discernibility and Rough Sets in Medicine: Tools and Applications. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway, 239.
93. Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E. & Zwiggelaar, R. 2008. A novel breast tissue density classification methodology. IEEE Transactions on Information Technology in Biomedicine 12(1), 55-65. (Pubitemid 351266643)
94. Pal, S. K. 2004. Pattern Recognition Algorithms for Data Mining. Chapman and Hall.
95. Pattaraintakorn, P. & Cercone, N. 2007. Integrating rough set theory and medical applications. Applied Mathematics Letters 21(4), 400-403.
96. Pawlak, Z. 1982. Rough sets. International Journal of Computing and Information Sciences 11, 341-356.
97. Pawlak, Z. 1984. Rough classification. International Journal of Man-Machine Studies 20, 469-483. (Pubitemid 15102839)
98. Pawlak, Z., Slowinski, K. & Slowinski, R. 1986. Rough classification of patients after highly selective vagotomy for duodenal ulcer. International Journal of Man Machine Studies 24, 413-433. (Pubitemid 16630925)
99. Pawlak, Z. 1991. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing.
100. Pawlak, Z. & Skowron, A. 1993. A rough set approach for decision rules generation. ICS Research Report 23/93, Warsaw University of Technology. Proceedings of the International Joint Conference on Artificial Intelligence'93 Workshop W12: The Management of Uncertainty in AI, France.
101. Pawlak, Z. 2003. Some issues on rough sets. Lecture Notes on Computer Science, Transactions on Rough Sets 1, Springer, Heidelberg, 1-53.
102. Pedrycz, W. 1999. Shadowed sets: bridging fuzzy and rough sets. In Rough-Fuzzy Hyridisation, Pal, S. K. & Skowron, A. (eds). Springer-Verlag, 179-199.
103. Peters, J. F., Skowron, A., Suraj, Z., Rzasa, W. & Bokowski, M. 2002. Clustering: a rough set approach to constructing information granules. In Proceedings of the 6th International Conference on Soft Computing and Distributed Processing, Rzeszow, Poland, 57-61.
104. Petrosino, A. & Ceccarelli, M. 2000. Unsupervised texture discrimination based on rough fuzzy sets and parallel hierarchical clustering. In Proceedings of the International Conference on Pattern Recognition (ICPR'00) 3 (September 03-08, 2000), IEEE Computer Society, Washington, DC.
105. Polkowski, L. & Skowron, A. 1998. Rough sets: a perspective. In Rough Sets in Knowledge Discovery 1: Methodology and Applications, Polkowski, L. & Skowron, A. (eds). Physica-Verlag, 31-56.
106. Qina, K. & Pei, Z. 2005. On the topological properties of fuzzy rough sets. Fuzzy Sets and Systems 151(3), 601-613. (Pubitemid 40305080)
107. Radzikowska, A. M. & Kerre, E. E. 2002. A comparative study of fuzzy rough sets. Fuzzy Sets and Systems 126(2), 137-155. Elsevier, Amsterdam. (Pubitemid 34116140)
108. Radzikowska, A. M. & Kerre, E. E. 2004. Fuzzy rough sets based on residuated lattices. Transactions on Rough Sets II, Lecture Notes in Computer Science (LNCS) 3135, 278-296. (Pubitemid 39749687)
109. Sarkar, M. 2000. Fuzzy-rough nearest neighbors algorithm. In Proceedings of the IEEE Conference on Systems Man and Cybernetics, Nashville, TN, USA, 3556-3561. (Pubitemid 32068787)
110. Sarkar, M. 2007. Fuzzy-rough nearest neighbors algorithm. Fuzzy Sets and Systems 158, 2123-2152.
111. Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton University Press.
112. Shan, D., Ishii, N., Hujun, Y., Allinson, N., Freeman, R., Keane, J. & Hubbard, S. 2002. Feature weights determining of pattern classification by using a rough genetic algorithm with fuzzy similarity measure. In Proceedings of Intelligent Data Engineering and Automated Learning, Manchester, UK, 544-550.
113. Shang, C. & Shen, Q. 2002. Rough feature selection for neural network based image classification. International Journal of Image and Graphics 2(4), 541-555.
114. Shao, M.-W. & Zhang, W.-X. 2004. Dominance relation and rules in an incomplete ordered information system. International Journal of Intelligent Systems 20(1), 13-20.
115. Shen, Q. & Chouchoulas, A. 2000. A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Engineering Applications of Artificial Intelligence 13(3), 263-278.
116. Shen, Q. & Chouchoulas, A. 2002. A rough-fuzzy approach for generating classification rules. Pattern Recognition 35(2), 2425-2438. (Pubitemid 34961613)
117. Shen, Q. & Jensen, R. 2004. Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern Recognition 37(7), 1351-1363. (Pubitemid 38653366)
118. Skowron, A. & Rauszer, C. 1992. The discernibility matrices and functions in information systems. In Intelligent Decision Support: Handbook of Applications and Advances to Rough Sets Theory, Slowinski, R. (ed.). Kluwer Academic, 331-362.
119. Skowron, A. 1993. Boolean reasoning for decision rules generation. In Proceedings of the 7th International Symposium ISMIS'93, Komorowski, J. & Ras, Z. (eds). Lecture Notes in Artificial Intelligence, Trondheim, Norway 689, 295-305. Springer-Verlag.
120. Skowron, A. & Stepaniuk, J. 1994. Generalized approximation spaces. In Proceedings of the 3rd International Workshop on Rough Sets and Soft Computing, San Jose, California, USA, 156-163.
121. Skowron, A. & Stepaniuk, J. 1996. Tolerance approximation spaces. Fundamenta Informaticae 27, 245-253. (Pubitemid 126715034)
122. Skowron, A., Pawlak, Z., Komorowski, J. & Polkowski, L. 2002. A rough set perspective on data and knowledge. In Handbook of Data Mining and Knowledge Discovery, Kloesgen, W., & Zytkow, J. (eds). Oxford University Press, 134-149.
123. Slowinski, R. & Vanderpooten, D. 1997. Similarity relations as a basis for rough approximations. In Advances in Machine Intelligence and Soft Computing, Wang, P. P. (ed.). Bookwrights, 17-33.
124. Slowinski, R. & Vanderpooten, D. 2000. A generalized definition of rough approximations based on similarity. IEEE Transactions on Knowledge and Data Engineering 12(2), 331-336.
125. Slowinski, K., Stefanowski, J. & Siwinski, D. 2002. Application of rule induction and rough sets to verification of magnetic resonance diagnosis. Fundamenta Informaticae 53 (3/4), 345-363.
126. Srinivasan, P., Ruiz, M. E., Kraft, D. H. & Chen, J. 1998. Vocabulary mining for information retrieval: rough sets and fuzzy sets. Information Processing & Management 37(1), 15-38.
127. Stefanowski, J. 1998. On rough set based approaches to induction of decision rules. In Rough Sets in Knowledge Discovery, Skowron, A. & Polkowski, L. (eds). 1, Physica-Verlag, 500-529.
128. Suckling, J., Partner, J., Dance, D. R., Astley, S. M., Hutt, I., Boggis, C. R. M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S. L., Taylor, Betal, P. & Savage, J. 1994. The mammographic image analysis society digital mammogram database. In International Workshop on Digital Mammography, York, UK, 211-221.
129. Swiniarski, R., Hunt, F., Chalvet, D. & Pearson, D. 1995. Intelligent data processing and dynamic process discovery using rough sets, statistical reasoning and neural networks in a highly automated production system. In Proceedings of the First European Conference on Application of Neural Networks in Industry, Helsinki, Finland.
130. Swiniarski, R. 1998. Rough sets Bayesian methods applied to cancer detection. In Proceeding of the First International Conference on Rough Sets and Soft Computing (RSCTC'98), Polkowski, L. & Skowron, A. (eds). LNAI 1424, 609-616. Springer-Verlag, 275-300. (Pubitemid 128093469)
131. Swiniarski, R. 1999. Rough sets and principal component analysis and their applications in data model building and classification. In Rough Fuzzy Hybridization: New Trends in Decision Making, Pal, S. K. & Skowron, A. (eds). Springer-Verlag, 275-300.
132. Swiniarski, R. & Skowron, A. 2003. Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 83-849.
133. Thangavel, K., Pethalakshmi, A. & Jaganathan, P. 2006. A comparative analysis of feature selection algorithms based on rough set theory. International Journal of Soft Computing 1(4), 288-294.
134. Thiele, H. 1998. 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.
135. Tsang, E. C. C., Chen, D., Yeung, D. S., Wang, X.-Z. & Lee, J. 2008. Attributes reduction using fuzzy rough sets. IEEE Transactions on Fuzzy Systems 16(5), 1130-1141.
136. Wang, J. & Wang, J. 2001. Reduction algorithms based on discernibility matrix: the ordered attributes method. Journal of Computer Science & Technology 16(6), 489-504. (Pubitemid 33110802)
137. Wang, Y., Ding, M., Zhou, C. & Zhang, T. 2005. A hybrid method for relevance feedback in image retrieval using rough sets and neural networks. International Journal of Computational Cognition 3(1), 78-87.
138. Wang, X., Yang, J., Teng, X. & Peng, N. 2005a. Fuzzy-rough set based nearest neighbor clustering classification algorithm. Lecture Notes in Computer Science 3613, 370-373. (Pubitemid 41480275)
139. Wang, Z., Shao, X., Zhang, G. & Zhu, H. 2005b. Integration of variable precision rough set and fuzzy clustering: an application to knowledge acquisition for manufacturing process planning. In Proceedings of the 10th Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Regina, Canada.
140. Wang, X., Yang, J., Teng, X., Xia, W. & Jensen, R. 2007. Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28(4), 459-471. (Pubitemid 44920436)
141. Wojdyllo, P. 1998. Wavelets, rough sets arificial neural networks in EEG analysis. In Proceedings of the First International Conference on Rough Sets and Soft Computing (RSCTC'98), Polkowski, L. & Skowron, A. (eds). LNAI 1424, 444-449. Springer-Verlag.
142. Wroblewski, J. 1995. Finding minimal reducts using genetic algorithms. In Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS'95), Wrightsville Beach, NC, USA, 186-189.
143. Wu, W. Z., Mi, J. S. & Zhang, W. X. 2003. Generalized fuzzy rough sets. Information Sciences 151, 263-282.
144. Wu, W. Z. & Zhang, W. X. 2004. Constructive and axiomatic approaches of fuzzy approximation operators. Information Sciences 159 (3-4), 233-254.
145. Wu, W. Z. 2005. A study on relationship between fuzzy rough approximation operators and fuzzy topological spaces. In Fuzzy Systems and Knowledge Discovery 2005, Wang, L. & Jin, Y. (eds). Lecture Notes in Artficial Intelligence 3613, 167-174. Springer, Heidelberg.
146. Wu, W. Z., Leung, Y. & Mi, J. S. 2005. On characterizations of (I, T)-fuzzy rough approximation operators. Fuzzy Sets and Systems 154(1), 76-102. (Pubitemid 40710188)
147. Wygralak, M. 1989. Rough sets and fuzzy sets - some remarks on interrelations. Fuzzy Sets and Systems 29(2), 241-243.
148. Yahia, M., Mahmod, R., Sulaiman, N. & Ahmad, F. 2000. Rough neural expert systems. Expert Systems with Applications 27(2), 87-99.
149. Yao, Y. Y. 1997. Combination of rough and fuzzy sets based on α-level sets. In Rough Sets and Data Mining: Analysis of Imprecise Data, Lin, T. Y. & Cereone, N. (eds). Kluwer Academic Publishers, 301-321.
150. Yeung, D. S., Chen, D., Tsang, E. C. C., Lee, J. W. T. & Xizhao, W. 2005. On the generalization of fuzzy rough sets. IEEE Transactions on Fuzzy Systems 13(3), 343-361. (Pubitemid 40914091)
151. Yi, G., Hu, H. & Lu, Z. 2005. Web document classification based on extended rough set. In Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technology (PDCAT), IEEE Computer Society, Washington, DC, 916-919.
152. Yun, O. & Ma, J. 2006. Land cover classification based on tolerant rough set. International Journal of Remote Sensing 27(14), 3041-3047. (Pubitemid 44219172)
153. Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8(3), 338-353.
154. Zhao, Y., Zhang, H. & Pan, Q. 2003. Classification using the variable precision rough set. In Proceedings of Rough Sets, Fuzzy Sets, Data Mining and Granular Computing 2003 2639, 350-353. Chongqing. (Pubitemid 36657983)
155. Zhao, S. & Zhang, Z. 2005. A generalized definition of rough approximation based on similarity in variable precision rough sets. In Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 3153-3156. (Pubitemid 41734462)
156. Zhao, Y., Zhou, X. & Tang, G. 2005. A rough set-based fuzzy clustering. In Proceedings of the Second Asia Information Retrieval Symposium, Jeju Island, Korea, 401-409.
157. Zhao, W. Q. & Zhu, Y. L. 2006. Classifying email using variable precision rough set approach. Lecture Notes in Artificial Intelligence 4062, 766-771.
158. Zheng, X. & Wang, J. 2008. Power transformer fault diagnosis based on variable precision rough set. In Proceedings of the 3rd International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Nanjing, China, 1353-1358.
159. Zhong, N., Dong, J. & Ohsuga, S. 2001. Using rough sets with heuristics for feature selection. Journal of Intelligent Information Systems 16(3), 199-214. (Pubitemid 32886812)
160. Ziarko, W. 1993. Variable precision rough set model. Journal of Computer and Systems Sciences 46(1), 39-59. (Pubitemid 23639120)
161. Ziarko, W. 2003. Acquisition of hierarchy-structured probabilistic decision tables and rules from data. Expert Systems 20(5), 305-310.