On Generalizing Rough Set Theory via using a Filter

• Serkan Narlı
• Ahmet Z. Ozcelik

• Rough set
• fuzzy set
• membership function
• knowledge representation and processing
• information theory

[1] J.Jarvinen, 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.
[2] J. Kelley, General Topology, Van Nostrand Company, 1955.
[3] ] E.F. Lashin, A.M. Kozae, A.A. Abo Khadra, T. Medhat, Rough set
theory for topological spaces, Internat. J. Approx. Reason. 40 (2005)
35-43 37
[4] T.Y. Lin, Topological and fuzzy rough sets, in: R. Slowinski (Ed.),
Decision Support by ExperienceÔÇö Application of the Rough Sets
Theory, Kluwer Academic Publishers, 1992, pp. 287-304.
[5] T.Y. Lin, Granular computing on binary relations II: Rough set
representations and belief functions, in: A. Skowron, L. Polkowski
(Eds.), Rough Sets In Knowledge Discovery, Physica-Verlag, 1998, pp.
121-140.
[6] T.Y. Lin, Granular computing on binary relation I: Data mining and
neighborhood systems, in: Rough Sets In Knowledge Discovery,
Physica-Verlag, 1998, pp. 107-121.
[7] Z. Pawlak, Rough sets, Int. J. Comput. Inform. Sci. 11 (5) (1982) 341-
356.
[8] Z. Pawlak, Granularity of knowledge, indiscernibility and rough sets,
in: Proceedings of 1998 IEEE International Conference on Fuzzy
Systems, Anchorage, AK, vol. 1, May 1998, pp. 106-110.
[9] Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data,
Kluwer Academic, Boston, 1991.
[10] Z. Pawlak, A. Skowron: Rough membership function, in: R. E Yeager,
M. Fedrizzi and J. Kacprzyk (eds.), Advaces in the Dempster-Schafer of
Evidence, Wiley, New York, 1994, 251-271
[11] J.A.Pomykala, Approximation operations in approximation space,
Bulletin of Polish Academy of Sciences, Mathematics, 35 (1987), 653-
662.
[12] U.Wybraniec-Skardowska, On a generalization of approximation space,
Bulletin of the Polish Academy of Sciences, Mathematics, 37 (1989),
51-61.
[13] Y.Y. Yao, Rough sets, neighborhood systems, and granular computing,
in: Proceedings of 1999 IEEE Canadian Conference on Electrical and
Computer Engineering, Edmonton, Alberta, Canada, May 1999, vol. 3,
pp. 1553-1558.
[14] Y.Y. Yao, Granular computing using neighborhood systems, in: R. Roy,
T. Furuhashi, P.K. Chawdhry (Eds.), Advances in Soft Computing:
Engineering Design and Manufacturing, Springer-Verlag, New York,
1999, pp. 539-553.
[15] Y.Y. Yao, Two views of the theory of rough sets infinite universes,
International Journal of Approximation Reasoning, 15 (1996), 291-317.
[16] Y.Y. Yao, On generalizing Pawlak approximation operators,
Proceedings of the First International Conference, RSCTC'98, LNAI
1424, 298-307, 1998.
[17] Y.Y.Yao, Constructive and algebraic methods of the theory of rough
sets, Information Sciences, 109(1998), 21-47.
[18] Y.Y.Yao, Generalized rough set models, in: Rough Sets in Knowledge
Discovery, Polkowski, L. and Skowron, A. (Eds.), Physica-Verlag,
Heidelberg, 286-318, 1998.
[19] Y.Y.Yao, Relational interpretations of neighborhood operators and
rough set approximation operators, Information Sciences, 111(1998),
239-259.
[20] Y.Y.Yao, Information granulation and rough set approximation,
International Journal of Intelligent Systems, 16(2001), 87-104.
[21] Y.Y.Yao, and Lin, T.Y. Generalization of rough sets using modal logic,
Intelligent Automation and Soft Computing, An International Journal,
2(1996), 103-120.
[22] Y.Y.Yao, , 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.
[23] Y.Y. Yao, On generalizing rough set theory, Proceedings of the 9th
International Conference on Rough Sets,Fuzzy Sets, Data Mining, and
Granular Computing (RSFDGrC 2003), LNAI 2639, Chongqing, China,
May 26-29, pp. 44-51, 2003.
[24] Y.Y.Yao, and Lin, T.Y. Generalization of rough sets using modal logic,
Intelligent Automation and Soft Computing, 2 (1996), 103-120.
[25] Y.Y.Yao, 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.
[26] Y.Y.Yao, 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.
[27] W.Zakowski, Approximations in the space (U;¤Ç), Demonstratio
Mathematica, 16 (1983), 761-769.