Tuning of a fuzzy classifier derived from data

Abstract

In our previous work (S. Abe and M.S. Lan, 1993), we developed a method for extracting fuzzy rules directly from numerical data for pattern classification. The performance of the fuzzy classifier developed by using this methodology was comparable to the average performance of neural networks. We further develop a least square method for tuning the sensitivity parameters of fuzzy membership functions by which the generalization ability of the classifier is improved. We evaluate the method using the Fisher iris data and data for numeral recognition of vehicle license plates. The results show that when the tuned sensitivity parameters are applied, the recognition rates are improved, to the extent that performance is comparable to or better than the maximum performance obtained by neural networks, but with shorter computational time.<>