Fast training of a fuzzy classifier with ellipsoidal regions

In this paper we discuss a fuzzy classifier with ellipsoidal regions which has a learning capability. First, we divide the training data for each class into several clusters. Then for each cluster we define a fuzzy rule with an ellipsoidal region around a cluster center. Using the training data for each cluster, we calculate the center and the covariance matrix of the ellipsoidal region for the cluster. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. We evaluate our method using the Fisher iris data and blood cell data. The recognition rates of our classifier are comparable to or better than the maximum recognition rates of the multilayered neural network classifier, and the training times for the blood cell data are three orders of magnitude shorter.