Genetic Type-2 Fuzzy Classifier Functions

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 classifi...

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Bibliographic Details
Main Authors: Celikyilmaz, A., Turksen, I.B.
Format: Conference Proceeding
Language:English
Subjects:
Area measurement
classification
Classification algorithms
Clustering algorithms
Educational institutions
Fuzzy sets
Fuzzy systems
Genetic algorithms
Industrial engineering
Shape
type-2 fuzzy functions
Uncertainty
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Summary: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.
DOI:10.1109/NAFIPS.2008.4531221