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A Survey on Evolutionary Computation Approaches to Feature Selection

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journal contribution
posted on 15.03.2021, 03:20 by Bing Xue, Mengjie Zhang, William Browne, X Yao
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.

History

Preferred citation

Xue, B., Zhang, M., Browne, W. N. & Yao, X. (2016). A Survey on Evolutionary Computation Approaches to Feature Selection. IEEE Transactions on Evolutionary Computation, 20(4), 606-626. https://doi.org/10.1109/TEVC.2015.2504420

Journal title

IEEE Transactions on Evolutionary Computation

Volume

20

Issue

4

Publication date

01/08/2016

Pagination

606-626

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published online

Contribution type

Article

Online publication date

30/11/2015

ISSN

1089-778X

eISSN

1941-0026

Language

en