Hybrid fuzzy-rough rule induction and feature selection

The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single integrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.

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