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Discernibility Matrix Based Attribute Reduction in Intuitionistic Fuzzy Decision Systems

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8170))

Abstract

Based on the theory of rough sets and intuitionistic fuzzy sets, this paper researches attribute reduction in intuitionistic fuzzy decision systems (IFDS). Firstly, we establish an intuitionistic fuzzy rough set model based on the similarity relation. Secondly, the discernibility matrix based on the maximal consistent block is constructed and an algorithm of attribute reduction is designed, which can eliminate the redundant information from the given IFDS. Finally, an illustrative example is employed to show the validity of the algorithm in this paper.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Shanxi Normal University, 041004, Linfen, Shanxi, P.R. China

    Qinrong Feng & Rui Li

Authors
  1. Qinrong Feng
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  2. Rui Li
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Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, viale Sarca 336/14, 20126, Milano, Italy

    Davide Ciucci

  2. Osaka University, 560-8531, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

  3. University of Regina, S4S 0A2, Regina, SK, Canada

    Yiyu Yao

  4. University of Warsaw, ul. Banacha, 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

  5. Chongqing Institute of Green and Intelligent Technology, CAS, 401122, Chongqing, China

    Guoyin Wang

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© 2013 Springer-Verlag Berlin Heidelberg

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Feng, Q., Li, R. (2013). Discernibility Matrix Based Attribute Reduction in Intuitionistic Fuzzy Decision Systems. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-41218-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41217-2

  • Online ISBN: 978-3-642-41218-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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