Local and Global Genetic Fuzzy Pattern Classifiers

Abstract

Fuzzy pattern classifiers are a recent type of classifiers making use of fuzzy membership functions and fuzzy aggregation rules, providing a simple yet robust classifier model. The fuzzy pattern classifier is parametric giving the choice of fuzzy membership function and fuzzy aggregation operator. Several methods for estimation of appropriate fuzzy membership functions and fuzzy aggregation operators have been suggested, but considering only fuzzy membership functions with symmetric shapes found by heuristically selecting a middle point from the learning examples. Here, an approach for learning the fuzzy membership functions and the fuzzy aggregation operator from data is proposed, using a genetic algorithm for search. The method is experimentally evaluated on a sample of several public datasets, and performance is found to be significantly better than existing fuzzy pattern classifier methods. This is despite the simplicity of the fuzzy pattern classifier model, which makes it interesting.