Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods

Volume 80 of the series Communications in Computer and Information Science pp 318-327

Feature Subset Selection for Fuzzy Classification Methods

  • Marcos E. CintraAffiliated withMathematics and Computer Science Institute, University of Sao Paulo (USP)
  • , Heloisa A. CamargoAffiliated withComputer Science Department, Federal University of São Carlos (UFSCar)

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The automatic generation of fuzzy systems have been widely investigated with several proposed approaches in the literature. Since for most methods the generation process complexity increases exponentially with the number of features, a previous feature selection can highly improve the process. Filters, wrappers and embedded methods are used for feature selection. For fuzzy systems it would be desirable to take the fuzzy granulation of the features domains into account for the feature selection process. In this paper a fuzzy wrapper, previously proposed by the authors, and a fuzzy C4.5 decision tree are used to select features. They are compared with three classic filters and the features selected by the original C4.5 decision tree algorithm, as an embedded method. Results using 10 datasets indicate that the use of the fuzzy granulation of features domains is an advantage to select features for the purpose of inducing fuzzy rule bases.


Feature selection fuzzy classification methods C4.5 Fuzzy C4.5 machine learning