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Volumn 84, Issue 3-4, 2008, Pages 403-427

Rough rule extracting from various conditions: Incremental and approximate approaches for inconsistent data

Author keywords

Approximate rule extracting; Improved discernibility matrix; Inconsistent rules; Incremental algorithm; Rough set

Indexed keywords

CHLORINE COMPOUNDS; FUZZY SETS; LAWS AND LEGISLATION; SET THEORY;

EID: 52649111659     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (9)

References (49)
  • 1
    • 52649133598 scopus 로고    scopus 로고
    • The ROSETTA home
    • The ROSETTA homepage, http://rosetta.lcb.uu.se/general/.
  • 2
    • 52649101162 scopus 로고    scopus 로고
    • Rough Analysis, http://www.lsi.upc.es/ealvarez/rough.html.
    • Rough Analysis
  • 3
    • 52649127024 scopus 로고    scopus 로고
    • home
    • The Rough Enough homepage, http://www.trolldata.no/renough/.
    • The Rough Enough
  • 7
    • 0027621699 scopus 로고    scopus 로고
    • Agrawal, R., Imielinski, T., Swami, A. N.: Mining Association Rules between Sets of Items in Large Databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993 (P. Buneman, S. Jajodia, Eds.), ACM Press, 1993.
    • Agrawal, R., Imielinski, T., Swami, A. N.: Mining Association Rules between Sets of Items in Large Databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26-28, 1993 (P. Buneman, S. Jajodia, Eds.), ACM Press, 1993.
  • 10
    • 9444239156 scopus 로고    scopus 로고
    • Certain rule learning of the inconsistent data
    • Bian, X.: Certain rule learning of the inconsistent data., Journal of East China Shipbuilding Institute, 12(1), 1998, 25-30.
    • (1998) Journal of East China Shipbuilding Institute , vol.12 , Issue.1 , pp. 25-30
    • Bian, X.1
  • 11
    • 0031334221 scopus 로고    scopus 로고
    • Selection of Relevant Features and Examples in Machine Learning
    • Blum, A., Langley, P.: Selection of Relevant Features and Examples in Machine Learning., Artif. Intell., 97(1-2), 1997, 245-271.
    • (1997) Artif. Intell , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.1    Langley, P.2
  • 12
    • 0242322799 scopus 로고    scopus 로고
    • Rough Set-Aided Keyword Reduction for Text Categorization
    • Chouchoulas, A., Shen, Q.: Rough Set-Aided Keyword Reduction for Text Categorization., Applied Artificial Intelligence, 15(9), 2001, 843-873.
    • (2001) Applied Artificial Intelligence , vol.15 , Issue.9 , pp. 843-873
    • Chouchoulas, A.1    Shen, Q.2
  • 13
    • 85149612939 scopus 로고
    • Fast Effective Rule Induction
    • Cohen, W. W.: Fast Effective Rule Induction., ICML, 1995.
    • (1995) ICML
    • Cohen, W.W.1
  • 14
    • 52649135250 scopus 로고    scopus 로고
    • A Simple, Fast, and Effictive Rule Learner
    • Cohen, W. W., Singer, Y.: A Simple, Fast, and Effictive Rule Learner., AAAI/IAAI, 1999.
    • (1999) AAAI/IAAI
    • Cohen, W.W.1    Singer, Y.2
  • 15
    • 0013326060 scopus 로고    scopus 로고
    • Feature Selection for Classification
    • Dash, M., Liu, H.: Feature Selection for Classification., Intell. Data Anal., 1(1-4), 1997, 131-156.
    • (1997) Intell. Data Anal , vol.1 , Issue.1-4 , pp. 131-156
    • Dash, M.1    Liu, H.2
  • 16
    • 52649120500 scopus 로고    scopus 로고
    • Online Ensemble Learning: An Empirical Study
    • Fern, A., Givan, R.: Online Ensemble Learning: An Empirical Study., ICML, 2000.
    • (2000) ICML
    • Fern, A.1    Givan, R.2
  • 17
    • 0002129041 scopus 로고    scopus 로고
    • Generating Accurate Rule Sets Without Global Optimization
    • Frank, E., Witten, I. H.: Generating Accurate Rule Sets Without Global Optimization., ICML, 1998.
    • (1998) ICML
    • Frank, E.1    Witten, I.H.2
  • 18
    • 0001905486 scopus 로고
    • Knowledge Acquisition under Uncertainty: A Rough Set Approach
    • Grzymala-Busse, J. W.: Knowledge Acquisition under Uncertainty: A Rough Set Approach., Journal of Intelligence and Robotic System., 1(1), 1988, 3-36.
    • (1988) Journal of Intelligence and Robotic System , vol.1 , Issue.1 , pp. 3-36
    • Grzymala-Busse, J.W.1
  • 20
    • 52649089906 scopus 로고    scopus 로고
    • Grzymala-Busse, J. W.: LERS - A Data Mining System., in: The Data Mining and Knowledge Discovery Handbook, 2005, 1347-1351.
    • Grzymala-Busse, J. W.: LERS - A Data Mining System., in: The Data Mining and Knowledge Discovery Handbook, 2005, 1347-1351.
  • 21
    • 52649136268 scopus 로고    scopus 로고
    • Hu, X.: knowledge discovery in database: an attribute-oriented rough set approach., issertation, Regina, 1995.
    • Hu, X.: knowledge discovery in database: an attribute-oriented rough set approach., issertation, Regina, 1995.
  • 22
    • 0029780174 scopus 로고    scopus 로고
    • Mining Knowledge Rules from Databases: A Rough Set Approach
    • Hu, X., Cercone, N.: Mining Knowledge Rules from Databases: A Rough Set Approach., ICDE, 1996.
    • (1996) ICDE
    • Hu, X.1    Cercone, N.2
  • 23
    • 0031378678 scopus 로고    scopus 로고
    • Learning Maximal Generalized Decision Rules via Discretization, Generalization, and Rough Set Feature Selection
    • Hu, X., Cercone, N.: Learning Maximal Generalized Decision Rules via Discretization, Generalization, and Rough Set Feature Selection., ICTAI, 1997.
    • (1997) ICTAI
    • Hu, X.1    Cercone, N.2
  • 24
    • 5844243848 scopus 로고
    • An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases
    • Hu, X., Cercone, N., Han, J.: An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases., RSKD, 1993.
    • (1993) RSKD
    • Hu, X.1    Cercone, N.2    Han, J.3
  • 25
    • 0029305145 scopus 로고
    • Rough Set Reduction of Attributes and their Domains for Neural Networks
    • Jelonek, J., Krawiec, K., Slowinski, R.: Rough Set Reduction of Attributes and their Domains for Neural Networks., Computational Intelligence, 11, 1995, 339-347.
    • (1995) Computational Intelligence , vol.11 , pp. 339-347
    • Jelonek, J.1    Krawiec, K.2    Slowinski, R.3
  • 26
    • 10944249572 scopus 로고    scopus 로고
    • Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
    • Jensen, R., Shen, Q.: Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches., IEEE Trans. Knowl. Data Eng., 16(12), 2004, 1457-1471.
    • (2004) IEEE Trans. Knowl. Data Eng , vol.16 , Issue.12 , pp. 1457-1471
    • Jensen, R.1    Shen, Q.2
  • 27
    • 0030231557 scopus 로고    scopus 로고
    • Efficient Incremental Induction of Decision Trees
    • Kalles, D., Morris, T.: Efficient Incremental Induction of Decision Trees., Machine Learning, 24(3), 1996, 231-242.
    • (1996) Machine Learning , vol.24 , Issue.3 , pp. 231-242
    • Kalles, D.1    Morris, T.2
  • 28
    • 0003036517 scopus 로고
    • The Feature Selection Problem: Traditional Methods and a New Algorithm
    • Kira, K., Rendell, L. A.: The Feature Selection Problem: Traditional Methods and a New Algorithm., AAAI, 1992.
    • (1992) AAAI
    • Kira, K.1    Rendell, L.A.2
  • 31
    • 0001172288 scopus 로고    scopus 로고
    • A Rough Set Framework for Data Mining of Propositional Default Rules
    • Møllestad, T., Skowron, A.: A Rough Set Framework for Data Mining of Propositional Default Rules., ISMIS, 1996.
    • (1996) ISMIS
    • Møllestad, T.1    Skowron, A.2
  • 32
    • 0037252858 scopus 로고    scopus 로고
    • Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation
    • Pal, S. K., Mitra, S., Mitra, P.: Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation., IEEE Trans. Knowl. Data Eng., 15(1), 2003, 14-25.
    • (2003) IEEE Trans. Knowl. Data Eng , vol.15 , Issue.1 , pp. 14-25
    • Pal, S.K.1    Mitra, S.2    Mitra, P.3
  • 34
    • 57749110329 scopus 로고
    • Rough Sets: Theoretical aspects and reasoning about data
    • Pawlak, Z.: Rough Sets: theoretical aspects and reasoning about data., Kluwer Academic Publishers, 1991.
    • (1991) Kluwer Academic Publishers
    • Pawlak, Z.1
  • 36
    • 14344263324 scopus 로고    scopus 로고
    • Towards tight bounds for rule learning
    • Rückert, U., Kramer, S.: Towards tight bounds for rule learning., ICML, 2004.
    • (2004) ICML
    • Rückert, U.1    Kramer, S.2
  • 37
    • 0002555963 scopus 로고
    • A Case Study of Incremental Concept Induction
    • Schlimmer, J. C., Fisher, D. H.: A Case Study of Incremental Concept Induction., AAAI, 1986.
    • (1986) AAAI
    • Schlimmer, J.C.1    Fisher, D.H.2
  • 38
    • 52649182344 scopus 로고    scopus 로고
    • Induction of Classification Rules from Imperfect Data
    • Shan, N., Hamilton, H. J., Cercone, N.: Induction of Classification Rules from Imperfect Data., ISMIS, 1996.
    • (1996) ISMIS
    • Shan, N.1    Hamilton, H.J.2    Cercone, N.3
  • 39
    • 0343975285 scopus 로고
    • An Incremental Learning Algorithm for Constructing Decision Rules
    • Springer-Verlag
    • Shan, N., Ziarko, W.: An Incremental Learning Algorithm for Constructing Decision Rules., Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 1993, 326-334.
    • (1993) Rough Sets, Fuzzy Sets and Knowledge Discovery , pp. 326-334
    • Shan, N.1    Ziarko, W.2
  • 40
    • 0000564750 scopus 로고
    • Boolean Reasoning for Decision Rules Generation
    • Skowron, A.: Boolean Reasoning for Decision Rules Generation., ISMIS, 1993.
    • (1993) ISMIS
    • Skowron, A.1
  • 42
    • 0033719032 scopus 로고    scopus 로고
    • A Generalized Definition of Rough Approximations Based on Similarity
    • Slowinski, R., Vanderpooten, D.: A Generalized Definition of Rough Approximations Based on Similarity., IEEE Trans. Knowl. Data Eng., 12(2), 2000, 331-336.
    • (2000) IEEE Trans. Knowl. Data Eng , vol.12 , Issue.2 , pp. 331-336
    • Slowinski, R.1    Vanderpooten, D.2
  • 44
    • 0037332841 scopus 로고    scopus 로고
    • Rough set methods in feature selection and recognition
    • Swiniarski, R. W., Skowron, A.: Rough set methods in feature selection and recognition., Pattern Recognition Letters, 24(6), 2003, 833-849.
    • (2003) Pattern Recognition Letters , vol.24 , Issue.6 , pp. 833-849
    • Swiniarski, R.W.1    Skowron, A.2
  • 45
    • 77952642202 scopus 로고
    • Incremental Induction of Decision Trees
    • Utgoff, P. E.: Incremental Induction of Decision Trees., Machine Learning, 4, 1989, 161-186.
    • (1989) Machine Learning , vol.4 , pp. 161-186
    • Utgoff, P.E.1
  • 46
    • 0031246271 scopus 로고    scopus 로고
    • Decision Tree Induction Based on Efficient Tree Restructuring
    • Utgoff, P. E., Berkman, N. C., Clouse, J. A.: Decision Tree Induction Based on Efficient Tree Restructuring., Machine Learning, 29(1), 1997, 5-44.
    • (1997) Machine Learning , vol.29 , Issue.1 , pp. 5-44
    • Utgoff, P.E.1    Berkman, N.C.2    Clouse, J.A.3
  • 47
    • 0026222433 scopus 로고
    • Learning Classification Rules from Database in the Context of Knowledge Acquisition and Representation
    • Yasdi, R.: Learning Classification Rules from Database in the Context of Knowledge Acquisition and Representation., IEEE Trans. Knowl. Data Eng., 3(3), 1991, 293-306.
    • (1991) IEEE Trans. Knowl. Data Eng , vol.3 , Issue.3 , pp. 293-306
    • Yasdi, R.1
  • 48
    • 0035416447 scopus 로고    scopus 로고
    • Using Rough Sets with Heuristics for Feature Selection
    • Zhong, N., Dong, J., Ohsuga, S.: Using Rough Sets with Heuristics for Feature Selection., J. Intell. Inf. Syst., 16(3), 2001, 199-214.
    • (2001) J. Intell. Inf. Syst , vol.16 , Issue.3 , pp. 199-214
    • Zhong, N.1    Dong, J.2    Ohsuga, S.3
  • 49
    • 52649117808 scopus 로고    scopus 로고
    • Rule Discovery from Databases with Decision Matrices
    • Ziarko, W., Cercone, N., Hu, X.: Rule Discovery from Databases with Decision Matrices., ISMIS, 1996.
    • (1996) ISMIS
    • Ziarko, W.1    Cercone, N.2    Hu, X.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.