Tuning of a fuzzy classifier derived from data

In our previous work we developed a method for extracting fuzzy rules directly from numerical data for pattern classification. The performance of the fuzzy classifier developed using this methodology was comparable to the average performance of neural networks. In this paper we further develop two methods, a feast squares method and an iterative method, for tuning the sensitivity parameters of fuzzy membership functions by which the generalization ability of the classifier is improved. We evaluate our methods 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 ex:tent that performance is comparable to or better than the maximum performance obtained by neural networks, but with shorter computational time.