A Survey on Evolutionary Computation Approaches to Feature Selection

Bing XUE, Mengjie ZHANG, Will N. BROWNE, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

1307 Citations (Scopus)

Abstract

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research. © 1997-2012 IEEE.
Original languageEnglish
Article number7339682
Pages (from-to)606-626
Number of pages21
JournalIEEE Transactions on Evolutionary Computation
Volume20
Issue number4
Early online date30 Nov 2015
DOIs
Publication statusPublished - Aug 2016
Externally publishedYes

Bibliographical note

This work was supported in part by the Marsden Fund of the New Zealand Government under Contract VUW1209, through the Royal Society of New Zealand, in part by the University Research Fund under Grant 210375/3557 and Grant 209861/3580 through the Victoria University of Wellington, in part by the Engineering and Physical Sciences Research Council under Grant EP/J017515/1, and in part by the Natural Science Foundation of China under Grant 61329302. The work of X. Yao was supported by the Royal Society Wolfson Research Merit Award.

Keywords

  • Classification
  • data mining
  • evolutionary computation
  • feature selection
  • machine learning

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