On generalizing rough set theory

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

Volumn 2639, Issue , 2003, Pages 44-51

  Yao, Y Y ^a  

^a UNIVERSITY OF REGINA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DATA MINING; FUZZY SETS; GRANULAR COMPUTING; MATHEMATICAL MODELS; ROUGH SET THEORY;

GENERALIZED ROUGH SET;

ROUGH SET THEORY; TECHNICAL PRESENTATIONS;

FORMULATIONS; GRANULAR COMPUTING;

EID: 8344253612     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-39205-x_6     Document Type: Conference Paper

References (30)

1. Cattaneo, G. Abstract approximation spaces for rough theories, in: Rough Sets in Knowledge Discovery, Polkowski, L. and Skowron, A. (Eds.), Physica-Verlag, Heidelberg, 59–98, 1998.
2. Chellas, B.F. Modal Logic: An Introduction, Cambridge University Press, Cambridge, 1980.
3. Cohn, P.M. Universal Algebra, Harper and Row Publishers, New York, 1965.
4. Järvinen, J. On the structure of rough approximations, Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing, LNAI 2475, 123–130, 2002.
5. Lin, T.Y. and Liu, Q. Rough approximate operators: axiomatic rough set theory, in Rough Sets, Fuzzy Sets and Knowledge Discovery, W.P. Ziarko (Ed.), Springer- Verlag, London, 256–260, 1994.
6. Pawlak, Z. Rough sets, International Journal of Computer and Information Sciences, 11, 341–356, 1982.
7. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Boston, 1991.
8. Pawlak, Z. and Skowron, A. Rough membership functions, in: Advances in the Dempster-Shafer Theory of Evidence, Yager, R.R., Fedrizzi, M. and Kacprzyk, J. (Eds.), John Wiley and Sons, New York, 251–271, 1994.
9. Pomykala, J.A. Approximation operations in approximation space, Bulletin of Polish Academy of Sciences, Mathematics, 35, 653–662, 1987.
10. Rasiowa, H. An Algebraic Approach to Non-classical Logics, North-Holland, Amsterdam, 1974.
11. Skowron, A. and Grzymala-Busse, J. From rough set theory to evidence theory, in: Advances in the Dempster-Shafer Theory of Evidence, Yager, R.R., Fedrizzi, M. and Kacprzyk, J. (Eds.), Wiley, New York, 193–236, 1994.
12. Wiweger, A. On topological rough sets, Bulletin of the Polish Academy of Sciences, Mathematics, 37, 89–93, 1989.
13. Wong, S.K.M. and Ziarko, W. Comparison of the probabilistic approximate classification and the fuzzy set model, Fuzzy Sets and Systems, 21, 357–362, 1987.
14. Wybraniec-Skardowska, U. On a generalization of approximation space, Bulletin of the Polish Academy of Sciences, Mathematics, 37, 51–61, 1989.
15. Wybraniec-Skardowska, U. Unit Operations, ZeszytyNaukowe Wyzszej Szkoly Pedagogicznej im Powstancow Slaskich w Opolu, Matematyka, XXVII, 113–129, 1992.
16. Yao, Y.Y. Two views of the theory of rough sets in finite universes, International Journal of Approximation Reasoning, 15, 291–317, 1996.
17. Yao, Y.Y. Constructive and algebraic methods of the theory of rough sets, Information Sciences, 109, 21–47, 1998.
18. Yao, Y.Y. Generalized rough set models, in: Rough Sets in Knowledge Discovery, Polkowski, L. and Skowron, A. (Eds.), Physica-Verlag, Heidelberg, 286–318, 1998.
19. Yao, Y.Y. Relational interpretations of neighborhood operators and rough set approximation operators, Information Sciences, 111, 239–259, 1998.
20. Yao, Y.Y. On generalizing Pawlak approximation operators, Proceedings of the First International Conference, RSCTC’98, LNAI 1424, 298–307, 1998.
21. Yao, Y.Y. Information granulation and rough set approximation, International Journal of Intelligent Systems, 16, 87–104, 2001.
22. Yao, Y.Y. Information granulation and approximation in a decision-theoretic model of rough sets, manuscript, 2002.
23. Yao, Y.Y. Probabilistic approaches to rough sets, manuscript, 2002.
24. Yao, Y.Y. Semantics of fuzzy sets in rough set theory, manuscript, 2002.
25. Yao, Y.Y. and Lin, T.Y. Generalization of rough sets using modal logic, Intelligent Automation and Soft Computing, An International Journal, 2, 103–120, 1996.
26. Yao, Y.Y. and Lingras, P.J. Interpretations of belief functions in the theory of rough sets, Information Sciences, 104, 81–106, 1998.
27. Yao, Y.Y. and Wang, T. On rough relations: an alternative formulation, Proceedings of The Seventh International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, LNAI 1711, 82–90, 1999.
28. Yao, Y.Y. and Wong, S.K.M. A decision theoretic framework for approximating concepts, International Journal of Man-machine Studies, 37, 793–809, 1992.
29. Yao, Y.Y., Wong, S.K.M. and Lin, T.Y. A review of rough set models, in: Rough Sets and Data Mining: Analysis for Imprecise Data, Lin, T.Y. and Cercone, N. (Eds.), Kluwer Academic Publishers, Boston, 47–75, 1997.
30. Zakowski, W. Approximations in the space (U, II), Demonstratio Mathematica, XVI, 761–769, 1983.