An improved evolutionary wrapper-filter feature selection approach with a new initialisation scheme

Abstract

Treated as one of the popular measures in information theory, fuzzy mutual information quantifies the amount of information that one random variable has about another one. Different from standard mutual information, fuzzy mutual information can deal with not only discrete-valued but also real-valued variables. Therefore, fuzzy mutual information has been recently used in evolutionary filter feature selection approaches to measure the correlation between the classes and the features, and the dependencies within a feature set. Typically, this way can be considered as computationally efficient but sometimes it may not contribute to the performance of a classification algorithm. To address this issue, an improved evolutionary wrapper-filter approach which integrates an initialisation scheme and a local search module based on fuzzy mutual information in differential evolution is proposed. According to a number of experiments conducted on several real-world benchmark datasets, the proposed approach does not only significantly improve the computational efficiency of an evolutionary computation technique but also the performance of a classification algorithm.