So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
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This paper aims at developing a theoretical framework for constructing ellipsoidal fuzzy classifiers with various input features from a data mining viewpoint.The proposed methodology for constructing fuzzy classification systems with ellipsoidal regions contains four parts, that is, rule-set initialization using a fully connected RBF neural network with an APC-III learning algorithm and cross entropy criterion; feature selection by using a simple and practical algorithm; determination of rule-set structure and representation using a generalized RBF neural network, where a fuzzy plus operator is employed as the activation function of the neurons at the output layer; and a regularization cost function addressing the trade-offbetween misclassification, recognition and generalization for optimizing the initial rule-set.