Attributes Reduction Using Fuzzy Rough Sets

Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets are mainly concentrated on the construction of approximation operators. Less effort has been put on the attributes reduction of databases with fuzzy rough sets. This paper mainly focuses on the attributes reduction with fuzzy rough sets. After analyzing the previous works on attributes reduction with fuzzy rough sets, we introduce formal concepts of attributes reduction with fuzzy rough sets and completely study the structure of attributes reduction. An algorithm using discernibility matrix to compute all the attributes reductions is developed. Based on these lines of thought, we set up a solid mathematical foundation for attributes reduction with fuzzy rough sets. The experimental results show that the idea in this paper is feasible and valid.

[1]  Qiang Shen,et al.  Centre for Intelligent Systems and Their Applications Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Sets and Systems ( ) – Fuzzy–rough Attribute Reduction with Application to Web Categorization , 2022 .

[2]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[3]  Yiyu Yao.  Combination of Rough and Fuzzy Sets Based on α-Level Sets , 1997 .

[4]  C. J. V. Rijsbergen,et al.  Rough Sets, Fuzzy Sets and Knowledge Discovery , 1994, Workshops in Computing.

[5]  Didier Dubois,et al.  Putting Rough Sets and Fuzzy Sets Together , 1992, Intelligent Decision Support.

[6]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[7]  Masahiro Inuiguchi,et al.  A New Proposal for Fuzzy Rough Approximations and Gradual Decision Rule Representation , 2004, Trans. Rough Sets.

[8]  Wen-Xiu Zhang,et al.  An axiomatic characterization of a fuzzy generalization of rough sets , 2004, Inf. Sci..

[9]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[10]  Rajen B. Bhatt,et al.  On fuzzy-rough sets approach to feature selection , 2005, Pattern Recognit. Lett..

[11]  Thorsten Kuhlmann,et al.  Intelligent decision support , 1998 .

[12]  Hung Son Nguyen,et al.  Discretization Problem for Rough Sets Methods , 1998, Rough Sets and Current Trends in Computing.

[13]  Jesús Manuel Fernández Salido,et al.  Rough set analysis of a general type of fuzzy data using transitive aggregations of fuzzy similarity relations , 2003, Fuzzy Sets Syst..

[14]  Wei-Zhi Wu,et al.  Constructive and axiomatic approaches of fuzzy approximation operators , 2004, Inf. Sci..

[15]  Gianpiero Cattaneo,et al.  Fuzzy Extension of Rough Sets Theory , 1998, Rough Sets and Current Trends in Computing.

[16]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[17]  Anna Maria Radzikowska,et al.  A comparative study of fuzzy rough sets , 2002, Fuzzy Sets Syst..

[18]  Xizhao Wang,et al.  On the generalization of fuzzy rough sets , 2005, IEEE Transactions on Fuzzy Systems.

[19]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[20]  Nehad N. Morsi,et al.  Axiomatics for fuzzy rough sets , 1998, Fuzzy Sets Syst..

[21]  M. Shaw,et al.  Induction of fuzzy decision trees , 1995 .

[22]  Bart Kosko,et al.  Fuzzy entropy and conditioning , 1986, Inf. Sci..

[23]  Wei-Zhi Wu,et al.  Generalized fuzzy rough sets , 2003, Inf. Sci..