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

Ren Diao, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


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.
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Fuzzy Systems
Publication statusPublished - 2012
EventFuzzy Systems - Queensland, Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012
Conference number: 21


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2012
Period10 Jun 201215 Jun 2012


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