A novel feature selection method using fuzzy rough sets

Abstract The fuzzy set theory and the rough set theory are two distinct but complementary theories that deal with uncertainty in data. The salient features of both the theories are encompassed in the domain of the fuzzy rough set theory so as to cope with the problems of vagueness and indiscernibility in real world data. This hybrid theory has been found to be a potential tool for data mining, particularly useful for feature selection. Most of the existing approaches to fuzzy rough sets are based on fuzzy relations. In this paper, a new definition for fuzzy rough sets in an information system based on the divergence measure of fuzzy sets is introduced. The properties of the fuzzy rough approximations are explored. Moreover, an algorithm for feature selection using the proposed approximations is presented and experimented using real data sets.

[1]  Qiang Shen,et al.  Fuzzy-rough data reduction with ant colony optimization , 2005, Fuzzy Sets Syst..

[2]  A. Nakamura,et al.  Fuzzy rough sets , 1988 .

[3]  Chris Cornelis,et al.  Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey , 2015, Fundam. Informaticae.

[4]  Yee Leung,et al.  Generalized fuzzy rough sets determined by a triangular norm , 2008, Inf. Sci..

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

[6]  Qiang Shen,et al.  Fuzzy-Rough Sets Assisted Attribute Selection , 2007, IEEE Transactions on Fuzzy Systems.

[7]  Xizhao Wang,et al.  Attributes Reduction Using Fuzzy Rough Sets , 2008, IEEE Transactions on Fuzzy Systems.

[8]  Seref Sagiroglu,et al.  The development of intuitive knowledge classifier and the modeling of domain dependent data , 2013, Knowl. Based Syst..

[9]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[10]  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 .

[11]  Qiang Shen,et al.  A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  Chris Cornelis,et al.  A comprehensive study of implicator-conjunctor-based and noise-tolerant fuzzy rough sets: Definitions, properties and robustness analysis , 2015, Fuzzy Sets Syst..

[13]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

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

[15]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

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

[17]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.

[18]  Zdzisław Pawlak,et al.  Rough set theory and its applications , 2002, Journal of Telecommunications and Information Technology.

[19]  Qiang Shen,et al.  Fuzzy Entropy-assisted Fuzzy-Rough Feature Selection , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[20]  Paulo Cortez,et al.  Modeling wine preferences by data mining from physicochemical properties , 2009, Decis. Support Syst..

[21]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[22]  S. N. Sivanandam,et al.  Introduction to Data Mining and its Applications , 2006, Studies in Computational Intelligence.

[23]  Inés Couso,et al.  Divergence measure between fuzzy sets , 2002, Int. J. Approx. Reason..

[24]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[25]  T. Iwiński.  Algebraic approach to rough sets , 1987 .