Discernibility matrix simplification for constructing attribute reducts

Information Sciences

Volumn 179, Issue 7, 2009, Pages 867-882

  Yao, Yiyu ^a   Zhao, Yan ^a  

^a UNIVERSITY OF REGINA   (Canada)

Author keywords

Algorithms; Attribute reduction; Discernibility matrix; Matrix simplification; Rough sets

Indexed keywords

COMPUTATIONAL METHODS; FUZZY SETS; HEURISTIC ALGORITHMS; MATHEMATICAL TRANSFORMATIONS; SET THEORY;

ATTRIBUTE REDUCTION; CONSTRUCTION ALGORITHMS; CONSTRUCTION METHODS; DISCERNIBILITY MATRIX; ELEMENTARY MATRICES; FINITE NUMBERS; GAUSSIAN ELIMINATION METHODS; MATRIX SIMPLIFICATION; REDUCTS; ROUGH SETS; SYSTEM OF LINEAR EQUATIONS;

ROUGH SET THEORY;

EID: 58249098798     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2008.11.020     Document Type: Article

References (37)

1. J.G. Bazan, H.S. Nguyen, S.H. Nguyen, P. Synak, J. Wroblewski, Rough set algorithms in classification problem, in: L. Polkowski, S. Tsumoto, T.Y. Lin (Eds.), Rough Set Methods and Applications, 2000, pp. 49-88.
2. Beaubouef T., Petry F.E., and Arora G. Information-theoretic measures of uncertainty for rough sets and rough relational databases. Information Sciences 109 (1998) 185-195
3. Bhatt R.B., and Gopal M. On the compact computational domain of fuzzy-rough sets. Pattern Recognition Letters 6 (2005) 1632-1640
4. Chen D.G., Wang C.Z., and Hu Q.H. A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Information Sciences 177 (2007) 3500-3518
5. Fishburn P.C. Utility Theory for Decision-Making (1970), John Wiley & Sons, New York
6. Guan J.W., and Bell D.A. Rough computational methods for information systems. Artificial Intelligence 105 (1998) 77-103
7. Han S.Q., and Wang J. Reduct and attribute order. Journal of Computer Science and Technology 19 (2004) 429-449
8. Hu F., and Wang G.Y. Quick reduction algorithm based on attribute order. Chinese Journal of Computers 30 (2007) 1429-1435
9. Hu Q.H., Xie Z.X., and Yu D.R. Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recognition 40 (2007) 3509-3521
10. Hu Q.H., Yu D.R., and Xie Z.X. Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters 27 (2006) 414-423
11. Jensen R., and Shen Q. Semantics-preserving dimensionality reduction: Rough and fuzzy-rough-based approaches. IEEE Transactions on Knowledge and Data Engineering 16 (2004) 1457-1471
12. Leung Y., Wu W.Z., and Zhang W.X. Knowledge acquisition in incomplete information systems: a rough set approach. European Journal of Operational Research 168 (2006) 164-180
13. Liang H.L., Wang J., and Yao Y.Y. User-oriented feature selection for machine learning. Computer Journal 50 (2007) 421-434
14. Liang J.Y., and Shi Z.Z. The information entropy, rough entropy and knowledge granulation in rough set theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12 (2004) 3746
15. Mi J.S., Wu W.Z., and Zhang W.X. Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159 (2004) 255-272
16. Nguyen H.S. On the decision table with maximal number of reducts. Electronic Notes in Theoretical Computer Science 82 (2003) 198-205
17. Nguyen H.S. Approximate Boolean reasoning: foundations and applications in data mining. Transactions on Rough Sets 5 (2006) 334-506 LNCS 4100
18. S.H. Nguyen, H.S. Nguyen, Some efficient algorithms for rough set methods, in: Proceedings of the International Conference on Information Processing and Management of Uncertainty on Knowledge Based Systems, 1996, pp. 1451-1456.
19. Pawlak Z. Rough sets. International Journal of Computer Information and Science 11 (1982) 341-356
20. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning About Data (1991), Kluwer Academic Publishers, Dordrecht, MA
21. Pawlak Z., and Skowron A. Rudiments of rough sets. Information Sciences 177 (2007) 3-27
22. Pawlak Z., and Skowron A. Rough sets and Boolean reasoning. Information Sciences 177 (2007) 41-73
23. Quafafou M. α-RST: a generalization of rough set theory. Information Sciences 124 (2000) 301-316
24. Roberts F.S. Measurement Theory (1976), Academic Press, New York
25. RSES. .
26. Skowron A., and Rauszer C. The discernibility matrices and functions in information systems. In: Slowiński R. (Ed). Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory (1992), Kluwer, Dordrecht
27. Swiniarski R.W. Rough sets methods in feature reduction and classification. International Journal of Applied Mathematics and Computer Science 11 (2001) 565-582
28. Wang G.Y., Yu H., and Yang D. Decision table reduction based on conditional information entropy. Chinese Journal of Computers 25 (2002) 759-766
29. Wang J., and Miao D.Q. Analysis on attribute reduction strategies of rough set. Chinese Journal of Computer Science and Technology 13 (1998) 189-192
30. Wang J., and Wang J. Reduction algorithms based on discernibility matrix: the ordered attributes method. Journal of Computer Science and Technology 16 (2001) 489-504
31. Wong S.K.M., and Ziarko W. On optimal decision rules in decision tables. Bulletin of Polish Academy of Sciences 33 (1985) 693-696
32. Wu W.Z., Zhang M., Li H.Z., and Mi J.S. Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Information Sciences 174 (2005) 143-164
33. Y.Y. Yao, Y. Zhao, J. Wang, S.Q. Han, A model of machine learning based on user preference of attributes, in: Proceedings of International Conference on Rough Sets and Current Trends in Computing, 2006, pp. 587-596.
34. Zhao K., and Wang J. A reduction algorithm meeting users' requirements. Journal of Computer Science and Technology 17 (2002) 578-593
35. Zhu W., and Wang F.Y. Reduction and axiomization of covering generalized rough sets. Information Sciences 152 (2003) 217-230
36. Zhu W., and Wang F.Y. On three types of covering-based rough sets. IEEE Transactions on Knowledge and Data Engineering 19 (2007) 1131-1144
37. W. Ziarko, Rough set approaches for discovering rules and attribute dependencies, in: W. Klösgen, J.M. Żytkow (Eds.), Handbook of Data Mining and Knowledge Discovery, Oxford, 2000, pp. 328-339.