A fuzzy neural network for pattern classification and feature selection

Abstract

A fuzzy neural network with memory connections for classification, and weight connections for selection is introduced, thereby solving simultaneously two major problems in pattern recognition: pattern classification and feature selection. The proposed network attempts to select important features from among the originally given plausible features, while maintaining the maximum recognition rate. The resulting value of weight connection represents the degree of importance of feature. Moreover, the knowledge acquired by the network can be described as a set of interpretable rules. The effectiveness of this new method has been validated by using Anderson's IRIS data. The results are: first, the use of two features selected by our method from among the original four in the proposed network results in virtually identical classifier performance; and second, the constructed classifier is described by three simple rules that are of if–then form.