Extraction of fuzzy rules from trained neural network using evolutionary algorithm

This paper presents our approach to the rule extraction problem from trained neural network. A method called REX is briefly described. REX acquires a set of fuzzy rules using an evolutionary algorithm. Evolutionary algorithm searches not only fuzzy rules, but also a description of fuzzy sets. The way of coding and evaluation process of an individual is presented. The method was tested using the following benchmark data sets: IRIS, WINE and Wisconsin Breast Cancer Diagnosis. On the basis of the experimental studies shown in this paper, we can conclude that rules obtained by REX can be easily understood by human - they include small number of premises, and their fidelity is very high. Obtained results are compared to other rule extraction methods.

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