Evolutionary computation for feature selection in classification problems

de la Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 (2013) Evolutionary computation for feature selection in classification problems. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3 (6). pp. 381-407. ISSN 1942-4787

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Abstract

Feature subset selection (FSS) has received a great deal of attention in statistics, machine learning, and data mining. Real world data analyzed by data mining algorithms can involve a large number of redundant or irrelevant features or simply too many features for a learning algorithm to handle them efficiently. Feature selection is becoming essential as databases grow in size and complexity. The selection process is expected to bring benefits in terms of better performing models, computational efficiency, and simpler more understandable models. Evolutionary computation (EC) encompasses a number of naturally inspired techniques such as genetic algorithms, genetic programming, ant colony optimization, or particle swarm optimization algorithms. Such techniques are well suited to feature selection because the representation of a feature subset is straightforward and the evaluation can also be easily accomplished through the use of wrapper or filter algorithms. Furthermore, the capability of such heuristic algorithms to efficiently search large search spaces is of great advantage to the feature selection problem. Here, we review the use of different EC paradigms for feature selection in classification problems. We discuss details of each implementation including representation, evaluation, and validation. The review enables us to uncover the best EC algorithms for FSS and to point at future research directions.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and Statistics Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023)
Depositing User:	Pure Connector
Date Deposited:	26 Nov 2013 13:08
Last Modified:	23 Jan 2024 01:25
URI:	https://ueaeprints.uea.ac.uk/id/eprint/44509
DOI:	10.1002/widm.1106

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