A harmony search based approach to hybrid fuzzy-rough rule induction

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 theories have been applied with much success to this area as well as to feature selection. Both applications involve the use of equivalence classes for a successful operation, it is therefore intuitive to combine them into a single integrated method. In this paper, a hybrid approach to fuzzy-rough rule induction is proposed. It employs the harmony search algorithm to generate and improvise the emerging rule sets, and thus, allows the method to converge to a concise, meaningful and accurate set of rules. The efficacy of the algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers.

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