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

Authors:
Anas Quteishat;Chee Peng Lim;Kay Sin Tan
Affiliations:
Department of Computer Engineering, Faculty of Engineering Technology, Al-Balqa' Applied University, Al-Salt, Jordan;School of Electrical and Electronic Engineering, University of Science, Nibong Tebal, Malaysia;Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Year:
2010

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Abstract

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.