A weighted fuzzy min-max neural network and its application to feature analysis

Abstract

In this paper, we present a modified fuzzy min-max neural network model and its application to feature analysis. In the model a hyperbox can be expanded without considering the hyperbox contraction process as well as the overlapping test. During the learning process, the feature distribution information is utilized to compensate the hyperbox distortion which may be caused by eliminating the overlapping area of hyperboxes in the contraction process. The weight updating scheme and the hyperbox expansion algorithm for the learning process are described. A feature analysis technique for pattern classification using the model is also presented. We define four kinds of relevance factors between features and pattern classes to analyze the saliency of the features in the learning data set.