Binary Competitive Swarm Optimizer Approaches For Feature Selection

Too, Jing Wei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah (2019) Binary Competitive Swarm Optimizer Approaches For Feature Selection. Computation, 7 (2). 01-17. ISSN 2079-3197

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Abstract

Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost.

Item Type:	Article
Uncontrolled Keywords:	Binary competitive swarm optimizer, Binary optimization, Classification, Competitive swarm optimizer, Feature selection
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	08 Dec 2020 14:08
Last Modified:	08 Dec 2020 14:08
URI:	http://eprints.utem.edu.my/id/eprint/24622
Statistic Details:	View Download Statistic

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