Abstract
In this paper, we discuss a robust training method for a fuzzy classifier with ellipsoidal regions. First, we define a fuzzy rule for each class. Next, we determine the weight for each training datum Dy the two-stage method in order to suppress the effect of outliers. Then, using these weights, we calculate the center and covariance matrix of the ellipsoidal region for each class and tune the Fuzzy rules. After tuning, to further improve generalization ability, we tune fuzzy rules between two classes using the training data in the class boundary. We demonstrate the effectiveness of our method using four benchmark data sets.
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