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Genetic type-2 fuzzy classifier functions

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2 Author(s)
Asli Celikyilmaz ; Department of Mechanical and Industrial Engineering, University of Toronto, 5 King¿s College Rd., ON, M5S 2H8, Canada ; I. Burhan Turksen

A new type-2 fuzzy classifier function system is proposed for uncertainty modeling using genetic algorithms - GT2FCF. Proposed method implements a three-phase learning strategy to capture the uncertainties in fuzzy classifier function systems induced by learning parameters, as well as fuzzy classifier functions. Hidden structures are captured with the implementation of improved fuzzy clustering. The optimum uncertainty interval of the type-2 fuzzy membership values are captured with a genetic learning algorithm. The results of the experiments show that the GT2FCF is comparable - if not superior- to well-known benchmark methods in terms of area under the receiver operating curve (AUC) performance measure.

Published in:

Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American

Date of Conference:

19-22 May 2008