Generalizations of rough sets: From crisp to fuzzy cases

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3066, Issue , 2004, Pages 26-37

  Inuiguchi, Masahiro ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; FUNCTIONS; FUZZY CONTROL; MATHEMATICAL MODELS; APPROXIMATION ALGORITHMS;

APPROXIMATION SPACES; EQUIVALENCE RELATIONS; FUZZY GENERALIZATIONS; DECISION RULES; FUZZY CASE; FUZZY-ROUGH SETS; GENERALIZED ROUGH SET; RULE EXTRACTION; TWO WAYS;

ROUGH SET THEORY;

EID: 9444222586     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-25929-9_3     Document Type: Conference Paper

References (19)

1. Alpigini, J. J., Peters, J. F., Skowron, A., Zhong, N.: Rough Sets and Current Trends in Computing, LNAI 2475, Springer Verlag, Berlin (2002).
2. Bonikowski, Z., Bryniarski, E., Wybraniec-Skardowska, U.: Extensions and Intensions in the Rough Set Theory. Information Sciences 107 (1998) 149-167
3. Dubois, D., Prade, H.: Rough Fuzzy Sets and Fuzzy Rough Sets. Int. J. General Syst. 17 (1990) 191-209.
4. Dubois, D., Prade, H.: Putting Rough Sets and Fuzzy Sets Together, in: R, Słowiński (Ed.) Intelligent Decision Support, Kluwer, Dordrecht (1992) 203-232.
5. Greco, S., Inuiguchi, M., Słowiński, R.: Rough Sets and Gradual Decision Rules. in: G. Wang et al.(Eds.) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Springer-Verlag, Berlin-Heidelberg (2003) 156-164.
6. Greco, S., Matarazzo, B., Słowiński, R.: The Use of Rough Sets and Fuzzy Sets in MCDM. in: Gal, T., Stewart, T. J., Hanne, T. (Eds.) Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications, Kluwer Academic Publishers, Boston, MA (1999) 14-1-14-59.
7. Inuiguchi, M.: Two Generalizations of Rough Sets and Their Fundamental Properties. Proceedings of 6th Workshop on Uncertainty Processing, September 24-27 Hejnice, Czech Republic (2003) 113-124.
8. Inuiguchi, M.: Classification- versus Approximation-oriented Fuzzy Rough Sets. Proceedings of IPMU 2004, July 4-9, Perugia, Italy (2004).
9. Inuiguchi, M., Hirano, S., Tsumoto, S.: Rough Set Theory and Granular Computing, Springer Verlag, Berlin (2003).
10. Inuiguchi, M., Tanino, T.: Two Directions toward Generalization of Rough Sets, in: M. Inuiguchi, S. Hirano, S. Tsumoto (Eds.) Rough Set Theory and Granular Computing, Springer Verlag, Berlin (2003) 47-57.
11. Inuiguchi, M., Tanino, T.: New Fuzzy Rough Sets Based on Certainty Qualification. in: K. Pal, L. Polkowski, A. Skowron (Eds.) Rough-Neural Computing, Springer-Verlag, Berlin-Heidelberg (2003) 278-296.
12. Inuiguchi, M., Tanino, T.: Function Approximation by Fuzzy Rough Sets, in: B. Bouchon-Meunier, L. Foulloy, R. R. Yager (Eds.) Intelligent Systems for Information Processing: From Representation to Applications, Elsevier, Amsterdam (2003) 93-104.
13. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Boston, MA (1991).
14. Słowiński, R., Vanderpooten, D.: A Generalized Definition of Rough Approximations Based on Similarity. IEEE Transactions on Data and Knowledge Engineering 12(2) (2000) 331-336.
15. Wang, G., Liu, Q., Yao, Y., Skowron, A.: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, LNAI 2639, Springer Verlag, Berlin (2003).
16. Yao, Y.Y.: Two Views of the Theory of Rough Sets in Finite Universes. International Journal of Approximate Reasoning 15 (1996) 291-317.
17. Yao, Y.Y.: Relational Interpretations of Neighborhood Operators and Rough Set Approximation Operators. Information Sciences 111 (1998) 239-259.
18. Yao, Y.Y., Lin, T.Y.: Generalization of Rough Sets Using Modal Logics. Intelligent Automation and Soft Computing 2(2) (1996) 103-120.
19. Ziarko, W.: Variable Precision Rough Set Model. J. Comput. Syst. Sci. 46(1) (1993) 39-59.