A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

Quteishat, Anas, Lim, Chee Peng and Tan, Kay Sin 2010, A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification, IEEE transactions on systems, man, and cybernetics part A : systems and humans, vol. 40, no. 3, pp. 641-650.

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Title A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
Author(s) Quteishat, Anas
Lim, Chee Peng
Tan, Kay Sin
Journal name IEEE transactions on systems, man, and cybernetics part A : systems and humans
Volume number 40
Issue number 3
Start page 641
End page 650
Total pages 10
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2010-05
ISSN 1083-4427
1558-2426
Keyword(s) fuzzy min-max (FMM) neural network
genetic algorithms (GAs)
pattern classification
rule extraction
Summary In 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.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048099

Document type: Journal Article
Collection: Institute for Frontier Materials
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