Predictor output sensitivity and feature similarity-based feature selection

This paper is concerned with a feature selection technique capable of generating an efficient feature set in a few selection steps. The feature saliency measure proposed is based on two factors, namely, the fuzzy derivative of the predictor output with respect to the feature and the similarity between the feature being considered and the feature set. The use of the fuzzy derivative enables modelling the vagueness that occurs in estimating the predictor output sensitivity. The feature similarity measure employed allows avoiding utilization of very redundant features. The experimental investigations performed on five real world problems have shown the effectiveness of the feature selection technique proposed. The technique developed removed a large number of features from the original data sets without reducing the classification accuracy of a classifier. In contrast, the accuracy of the classifiers utilizing the reduced feature sets was higher than those exploiting all the original features.