An integrated mechanism for feature selection and fuzzy rule extraction for classification

Yi Cheng Chen*, Nikhil R. Pal, I. Fang Chung

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

63 Scopus citations

Abstract

In our view, the most important characteristic of a fuzzy rule-based system is its readability, which is seriously affected by, among other things, the number of features used to design the rule base. Hence, for high-dimensional data, dimensionality reduction through feature selection (not extraction) is very important. Our objective, here, is not to find an optimal rule base for classification but to select a set of useful features that may solve the classification problem. For this, we present an integrated mechanism for simultaneous extraction of fuzzy rules and selection of useful features. Since the feature selection method is integrated into the rule base formation, our scheme can account for possible subtle nonlinear interaction between features, as well as that between features and the tool, and, consequently, can select a set of useful features for the classification job. We have tried our method on several commonly used datasets as well as on a synthetic dataset with dimension varying from 4 to 60. Using a ten-fold cross-validation setup, we have demonstrated the effectiveness of our method.

Original languageEnglish
Article number6112676
Pages (from-to)683-698
Number of pages16
JournalIEEE Transactions on Fuzzy Systems
Volume20
Issue number4
DOIs
StatePublished - 2012

Keywords

  • Dimensionality reduction
  • feature modulators
  • feature selection
  • fuzzy rules

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