Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods

This paper studies the identification of fuzzy classifiers and function estimators focusing on improving their interpretability while maintaining their accuracy. Advances of various methods, such as, input variable selection, appropriate initialization algorithms, evolutionary algorithms and simplification techniques are hybridized to form a framework capable of identifying interpretable and accurate fuzzy models (FMs). FMs are initialized by two algorithms. Modified Gath-Geva (MGG) is used for function estimation and C4.5 for classification problems. The initialized FMs go through a three-step GA-based optimization, in which the adequate structure and parameters of FMs are searched. The proposed fitness function makes the favoring of simple FMs possible. Furthermore, the rule base is made more comprehensible by reducing the number of conditions in the rules. The validity of FMs is verified through studying several well-known benchmark problems. The results indicate, that by means of the proposed framework, interpretable, yet accurate FMs are obtained.