Binary Anarchic Society Optimization for Feature Selection

dc.authoridSarac, Esra/0000-0002-2503-0084
dc.contributor.authorKılıç, Umit
dc.contributor.authorSarac Essiz, Esra
dc.contributor.authorKaya Keles, Mumine
dc.date.accessioned2025-01-06T17:36:27Z
dc.date.available2025-01-06T17:36:27Z
dc.date.issued2023
dc.description.abstractDatasets comprise a collection of features; however, not all of these features may be necessary. Feature selection is the process of identifying the most relevant features while eliminating redundant or irrelevant ones. To be effective, feature selection should improve classification performance while reducing the number of features. Existing algorithms can be adapted and modified into feature selectors. In this study, we introduce the implementation of the Anarchic Society Optimization algorithm, a human-inspired algorithm, as a feature selector. This is the first study that utilizes the binary version of the algorithm for feature selection. The proposed Binary Anarchic Society Algorithm is evaluated on nine datasets and compared to three known algorithms: Binary Genetic Algorithm, Binary Particle Swarm Optimization, and Binary Gray Wolf Optimization. Additionally, four traditional feature selection techniques (Info Gain, Gain Ratio, Chi-square, and ReliefF) are incorporated for performance comparison. Our experiments highlight the competitive nature of the proposed method, suggesting its potential as a valuable addition to existing feature selection techniques.
dc.identifier.doi10.59277/ROMJIST.2023.3-4.08
dc.identifier.endpage364
dc.identifier.issn1453-8245
dc.identifier.issue3-4
dc.identifier.scopus2-s2.0-85175796765
dc.identifier.scopusqualityQ1
dc.identifier.startpage351
dc.identifier.urihttps://doi.org/10.59277/ROMJIST.2023.3-4.08
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1883
dc.identifier.volume26
dc.identifier.wosWOS:001083522800008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEditura Acad Romane
dc.relation.ispartofRomanian Journal of Information Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectartificial intelligence
dc.subjectanarchic society optimization
dc.subjectfeature selection
dc.subjectmeta-heuristic
dc.subjectswarm intelligence algorithms
dc.titleBinary Anarchic Society Optimization for Feature Selection
dc.typeArticle

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