Fuzzy classifier design using genetic algorithms

• Published in: Pattern recognition Vol. 40; no. 12; pp. 3401 - 3414
• Main Authors: Zhou, Enwang, Khotanzad, Alireza
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.12.2007; Elsevier Science
• Subjects: Applied sciences; Exact sciences and technology; Fuzzy classifier; Fuzzy rule extraction; Genetic algorithms; Information, signal and communications theory; Optimization of fuzzy parameters; Pattern classification; Pattern recognition; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Optimization of fuzzy parameters; Fuzzy classifier; Fuzzy rule extraction; Pattern classification; Genetic algorithms; Performance evaluation; Automatic classification; Membership function; Expert system; Pattern recognition; Signal classification; Optimization; Learning; Genetic algorithm; Database
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2007.03.028

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.