Possibilistic fuzzy classification using neural networks

This paper examines the performance of a possibilistic fuzzy classification method where the possibility area of each class is identified by the learning of a multilayer feedforward neural network. The possibility areas of different classes may overlap one another in the pattern space. The overlapping region of the possibility areas of two classes is viewed as the fuzzy boundary between those classes. Thus our method does not always assign an input pattern to a single class. When an input pattern is on a fuzzy boundary, a set of possible classes is indicated by the trained neural network as the classification result for that pattern. In this paper, we first illustrate our possibilistic fuzzy classification method. Next we examine its performance by computer simulations on real-world test problems. Then we discuss the relation between our method and the reject option. Finally we extend our method to the case where a rejection penalty is explicitly given in classification problems