Analysis on classification performance of rough set based reducts

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4099 LNAI, Issue , 2006, Pages 423-433

  Hu, Qinghua ^a   Li, Xiaodong ^a   Yu, Daren ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; LEARNING SYSTEMS; MATHEMATICAL MODELS; SET THEORY;

FEATURE SUBSET SELECTION; RANDOM SUBSPACE; REDUCTION SELECTION STRATEGIES; THEORETIC ANALYSIS;

FEATURE EXTRACTION;

EID: 33749543946     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11801603_46     Document Type: Conference Paper

References (39)

1. Kim D., Bang S. Y.: A handwritten numeral character classification using tolerant rough set. IEEE transactions on PAMI. 22 (2000) 923-937
2. Kim D.: Data classification based on tolerant rough set. Pattern recognition. 34 (2001) 1613-1624
3. Greco S., Matarazzo B., Slowinski R.: Rough sets theory for multicriteria decision analysis. European journal of operational research. 129 (2001) 1-47
4. Yao Y.: Relational interpretations of neighborhood operators and rough set approximation operators. Information sciences. 111 (1998 ) 239-259
5. Wu W. Z., Zhang W. X.: Neighborhood operator systems and approximations. Information sciences. 144 (2002) 201-217
6. Slowinski R., Vanderpooten D.: A generalized definition of rough approximations based on similarity. IEEE transactions on knowledge and data engineering. 12 (2000) 331-336
7. Dubois D., Prade H.: Rough fuzzy sets and fuzzy rough sets. International Journal of general systems. 17 (1990) 191-209
8. Dubois D., Prade H.: Putting fuzzy sets and rough sets together, in: R. Slowiniski (Ed.), Ittelligent Decision support, Kluwer Academic, Dordrecht, 1992, 203-232
9. Boixader D., Jacas J., Recasens J.: Upper and lower approximations of fuzzy sets. International journal of general systems. 29 (2000) 555-568
10. Wu W., Mi J., Zhang W.: Generalized fuzzy rough sets. Information sciences. 151 (2003) 263-282
11. Yeung D. S., Chen D. G., Tsang ECC, et al.: On the generalization of fuzzy rough sets. IEEE transactions on fuzzy systems. 13 (2005) 343-361
12. Duntsch I., Gediga G.: Uncertainty measures of rough set prediction. Artificial intelligence. 106 (1998) 109-137
13. Pawlak Z.: Rough sets, decision algorithms and Bayes' theorem. European Journal of Operational Research. 136 (2002) 181-189
14. Ziarko W.: Variable Precision Rough Set Model. J. Computer and System Sciences. 46 (1993) 39-59
15. Wei L., Zhang W.: Probabilistic rough sets characterized by fuzzy sets. International journal of uncertainty, fuzziness and knowledge based systems. 12 (2004) 47-60
16. Hu Q., Yu D., and Xie Z.: Fuzzy probabilistic approximation spaces and their information measures. IEEE transactions on fuzzy systems. 14 (2006) 191-201
17. Skowron R., Rauszer C., 1991. The discernibility matrices and functions in information systems. Intelligent ecision Support: Handbook of Applications and Advances of Rough Set Theory, 331-362
18. Hu X., Cercone N.: Learning in Relational Databases: A Rough Set Approach. Computational Intelligence. 11(1995) 323-338
19. Jelonek J., Krawiec K., Slowinski R.: Rough set reduction of attributes and their domains for neural networks. Computational Intelligence. 11 (1995) 339-347
20. Wang J., Miao D.: Analysis of attribute reduction strategies of rough set. Journal of computer science and technology. 13 (1998) 189-193
21. Bazan J. G., Skowron A., and Synak P.: Dynamic reducts as a tool for extracting laws from decision tables. LNAI, vol. 869, pp.346-355. Springer-Verlag, 1994
22. Wroblewski J.: Finding minimal reducts using genetic algorithms. In Proc. Second International Joint Conference on Information Sciences, pages 186-189, Sept. 1995
23. Jensen R., Shen Q.: Fuzzy-rough sets for descriptive dimensionality reduction. FUZZ-IEEE'02. Vol.1, 12-17, pp: 29-34
24. Jensen R., Shen Q.: Fuzzy-rough attribute reduction with application to web categorization. Fuzzy sets and systems. 141 (2004) 469-485
25. Shen Q., Jensen R.: Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern recognition. 37 (2004) 1351-1363
26. Bhatt R. B., Gopal M.: On fuzzy-rough sets approach to feature selection. Pattern recognition letters. 26 (2005) 965-975
27. Hu Q. H., Yu D. R.: Entropies of fuzzy indiscernibility relation and its operations. International Journal of uncertainty, fuzziness and knowledge-based systems. 12 (2004) 575-589
28. Hu Q. H., Yu D. R., Xie Z. X.: Information-preserving hybrid data reduction based on fuzzy rough techniques. Pattern recognition letters. 27 (2006) 414-423
29. Pawlak Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht. 1991
30. Hu X.: 1995, knowledge discovery in database: an attribute-oriented rough set method. Ph.D. Thesis. University of Regina.
31. Slezak D.: 2001, Approximate decision reducts. Ph.D. Thesis. Warsaw University.
32. Jensen R., Shen Q.: Semantics-preserving dimensionality reduction: rough and fuzzyrough based approaches. IEEE trans. on knowl. and data engin. 16 (2004) 1457-1471
33. Hassanien E.: Rough Set Approach for Attribute Reduction and Rule Generation: A Case of Patients With Suspected Breast Cancer. Journal of the American society for information science and technology. 55 (2004) 954-962
34. Domingos P.: The role of Occam's razor in knowledge discovery. Data mining and knowledge discovery. 3 (1999) 409-425
35. Skowron R, Rauszer C.: The discernibility matrices and functions in information systems. Intelligent decision support - Handbook of applications and advances of the rough sets theory. R. Slowinski (edit) 1991, 331-362.
36. Hu X.: Using rough set theory and database operations to construct a good ensemble of classifiers for data mining applications. 2001 ICDM. 233-240.
37. Wu Q., David B., Martin M.: Multiknowledge for decision making. Knowledge and information systems. 7 (2005) 246-266
38. Hu Q. H., Yu D. R., Wang M. Y.: Constructing rough decision forests. D. Slezak et al. (Eds.): RSFDGrC 2005, LNAI. 3642 (2005) 147-156
39. Ho T. K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on pattern analysis and machine intelligence. 20 (1998) 832-844