A New Approach for Handling Classification Problems Based on Fuzzy Information Gain Measures

In this paper, we present a new method for handling classification problems based on fuzzy information gain measures. First, we propose a fuzzy information gain measure of a feature with respect to a set of training instances. Then, based on the proposed fuzzy information gain measure, we present an algorithm for constructing membership functions, calculating the class degree of each subset of training instances with respect to each class and calculating the fuzzy entropy of each subset of training instances, where each subset of training instances contains a part of the training instances whose values of a specific feature fall in the support of a specific fuzzy set of this feature. Finally, we propose an evaluating function for classifying testing instances. The proposed method can deal with both numeric and nominal features. It gets higher average classification accuracy rates than the existing methods.