{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T10:56:57Z","timestamp":1776769017352,"version":"3.51.2"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T00:00:00Z","timestamp":1560470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Higher Education Malaysia","award":["GLuar\/STEVIA\/2016\/FKE-CeRIA\/l00009"],"award-info":[{"award-number":["GLuar\/STEVIA\/2016\/FKE-CeRIA\/l00009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computation7020031","type":"journal-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T03:16:07Z","timestamp":1560482167000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Binary Competitive Swarm Optimizer Approaches for Feature Selection"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-1038","authenticated-orcid":false,"given":"Jingwei","family":"Too","sequence":"first","affiliation":[{"name":"Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul Rahim","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norhashimah","family":"Mohd Saad","sequence":"additional","affiliation":[{"name":"Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.knosys.2017.12.037","article-title":"Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems","volume":"145","author":"Mafarja","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.eswa.2018.08.051","article-title":"Binary butterfly optimization approaches for feature selection","volume":"116","author":"Arora","year":"2019","journal-title":"Expert Syst. 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