A new standard error based artificial bee colony algorithm and its applications in feature selection

Basit Öğe Kaydı
dc.authoridHanbay, Kazım/0000-0003-1374-1417
dc.authorwosidHanbay, Kazım/J-3848-2014
dc.contributor.authorHanbay, Kazim
dc.date.accessioned2024-08-04T20:50:14Z
dc.date.available2024-08-04T20:50:14Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractFeature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other wellknown algorithms.(c) 2021 The Author. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.identifier.doi10.1016/j.jksuci.2021.04.010
dc.identifier.endpage4567en_US
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85106217468en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4554en_US
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2021.04.010
dc.identifier.urihttps://hdl.handle.net/11616/99937
dc.identifier.volume34en_US
dc.identifier.wosWOS:000841071400003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of King Saud University-Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial bee colonyen_US
dc.subjectOptimizationen_US
dc.subjectShannon entropyen_US
dc.subjectFeature selectionen_US
dc.titleA new standard error based artificial bee colony algorithm and its applications in feature selectionen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu