A Survey on Evolutionary Computation Approaches to Feature Selection

Feature selection is an important task in data miningand machine learning to reduce the dimensionality of the dataand increase the performance of an algorithm, such as a clas-sification algorithm. However, feature selection is a challengingtask due mainly to the large search space. A variety of methodshave been applied to solve feature selection problems, whereevolutionary computation techniques have recently gained muchattention and shown some success. However, there are no compre-hensive guidelines on the strengths and weaknesses of alternativeapproaches. This leads to a disjointed and fragmented fieldwith ultimately lost opportunities for improving performanceand successful applications. This paper presents a comprehensivesurvey of the state-of-the-art work on evolutionary computationfor feature selection, which identifies the contributions of thesedifferent algorithms. In addition, current issues and challengesare also discussed to identify promising areas for future research. Index Terms—Evolutionary computation, feature selection,classification, data mining, machine learning. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.