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A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
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posted on 2010-05-01, 00:00 authored by A Quteishat, Chee Peng LimChee Peng Lim, K TanIn this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don't care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.
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Journal
IEEE transactions on systems, man, and cybernetics part A : systems and humansVolume
40Issue
3Pagination
641 - 650Publisher
IEEELocation
Piscataway, N. J.ISSN
1083-4427eISSN
1558-2426Language
engPublication classification
C1.1 Refereed article in a scholarly journalUsage metrics
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