Research on Extension of the Fuzzy Rough Set Theory

Yu Zhang

doi:10.4028/www.scientific.net/AMR.532-533.1472

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Research on Extension of the Fuzzy Rough Set Theory
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Research on Extension of the Fuzzy Rough Set...

Research on Extension of the Fuzzy Rough Set Theory

Abstract:

With the probability rules and the degree of coverage of elements in the partition set, and the combination of the Fuzzy Set Theory and Rough Set Theory, a new extension of Fuzzy Rough Set theory was proposed. It is defined the maximum of Rough Set membership function, the minimum of Rough Set membership function, the average of Rough Set membership function, the upper minimum of Rough Set membership function and the lower maximum of Rough Set membership function. The properties of the extension for the Rough Set membership functions are also given. This made Fuzzy Set Theory and Rough Set Theory complemented each other and provided a new way to deal with incomplete data.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1472-1476

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1472

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Online since:

June 2012

Authors:

Yu Zhang

Keywords:

Coverage, Fuzzy Set, Membership Function, Rough Set Theory (RST)

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References

[1] G. J Klir ,B. Yuan. Fuzzy Sets and Fuzzy Logic: Thery and Application, Prentice Hall, 1New Jersey. (1995).

Google Scholar

[2] Y.Y. Yao, J.P. Zhang. Interpreting Fuzzy Membership Function in the Theory of Rough Sets. RSCTC 2000, LNAI2005, 2001pp. 82-89.

Google Scholar

[3] Hsuan-Shih Lee. A Measure of Fuzzy in Rough Sets. KES 2002, IOS Press, 2002, pp.80-84.

Google Scholar

[4] Xiao-Ping Yang. An Interpretation of Rough Sets in Incomplete Information System within Intuitionistic Fuzzy Sets. RSKT2009, LNCS5589,. 2009pp. 326-333.

DOI: 10.1007/978-3-642-02962-2_41

Google Scholar

[5] Denise Guliato and Jean Carlo de Sousa SAantos. Grsnular Computing and Rough Sets to Generate Fuzzy Rules. ICIAR 2009, LNCS, pp.317-326, (2009).

DOI: 10.1007/978-3-642-02611-9_32

Google Scholar

[6] K. Chakrabarty, Ranjit Biswas, and Sudarsan Nanda. Fuzziness in Rough Sets, Fuzzy S, ets and Systems 110(2000), pp.247-251.

DOI: 10.1016/s0165-0114(97)00414-4

Google Scholar

[7] HU Jun, WANG Guoying. ZHANG Qinghua. Covering Based Generalized Rough Fuzzy Set Model. Journal of Software, 2010, 21(5).

DOI: 10.3724/sp.j.1001.2010.2010.03624

Google Scholar

[8] Mi JS Leung , Y Zhao , HY Feng T. Generalized fuzzy rough sets determined by a triangular norm. Information Sciences, 2008(16).

DOI: 10.1016/j.ins.2008.03.013

Google Scholar