Simultaneous feature selection and extraction using fuzzy rough sets

Abstract

In this chapter, a novel dimensionality reduction method, based on fuzzy rough sets, is presented, which simultaneously selects attributes and extracts features using the concept of feature significance. The method is based on maximizing both relevance and significance of the reduced feature set, whereby redundancy therein is removed. The chapter also presents classical and neighborhood rough sets for computing relevance and significance of the feature set and compares their performance with that of fuzzy rough sets based on the predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. The effectiveness of the proposed fuzzy rough set-based dimensionality reduction method, along with a comparison with existing attribute selection and feature extraction methods, is demonstrated on real-life data sets.