Focusing on Interpretability and Accuracy of a Genetic Fuzzy System

Abstract

This research work presents a new approach for fuzzy system building taking into account the accuracy and interpretability of the system. One difficulty in the handling of high-dimensional problems by fuzzy rule-based systems is the exponential increase in the number of rules and in the number of conditions in the antecedent part of the rule. Thus, as first step of the proposed approach we apply a feature selection process in order to exclude irrelevant variables. Besides that, dimensionality reduction generally promotes the accuracy and comprehensibility of the system. After that, a genetic algorithm is used for deriving short and comprehensible fuzzy rules. Finally another genetic algorithm is used for optimizing the rule base obtained in the last step, excluding unnecessary and redundant rules. The fitness function of the algorithms consider both accuracy and interpretability of the fuzzy model and the use of "don't care" condition allows to generate more comprehensible with high generalization capacity. The application domain is multidimensional fuzzy pattern classification. By computational simulation in some well-know datasets, we can see that the proposed approach is able to generate compact fuzzy rule bases with high classification ability. When compared to other fuzzy building method reported in the literature, our proposed method presented a good performance