Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2405
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTürkoğlu, Bahaeddin-
dc.contributor.authorUymaz, Sait Ali-
dc.contributor.authorKaya, Ersin-
dc.date.accessioned2022-05-23T20:22:40Z-
dc.date.available2022-05-23T20:22:40Z-
dc.date.issued2022-
dc.identifier.issn1568-4946-
dc.identifier.issn1872-9681-
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2022.108630-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/2405-
dc.description.abstractIn this study, binary versions of the Artificial Algae Algorithm (AAA) are presented and employed to determine the ideal attribute subset for classification processes. AAA is a recently proposed algorithm inspired by microalgae's living behavior, which has not been consistently implemented to determine ideal attribute subset (feature selection) processes yet. AAA can effectively look into the feature space for ideal attributes combination minimizing a designed objective function. The proposed binary versions of AAA are employed to determine the ideal attribute combination that maximizes classification success while minimizing the count of attributes. The original AAA is utilized in these versions while its continuous spaces are restricted in a threshold using an appropriate threshold function after flattening them. In order to demonstrate the performance of the presented binary artificial algae algorithm model, an experimental study was conducted with the latest seven highperformance optimization algorithms. Several evaluation metrics are used to accurately evaluate and analyze the performance of these algorithms over twenty-five datasets with different difficulty levels from the UCI Machine Learning Repository. The experimental results and statistical tests verify the performance of the presented algorithms in increasing the classification accuracy compared to other state-of-the-art binary algorithms, which confirms the capability of the AAA algorithm in exploring the attribute space and deciding the most valuable features for classification problems. (C) 2022 Elsevier B.V. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature selectionen_US
dc.subjectArtificial Algae Algorithmen_US
dc.subjectMetaheuristicsen_US
dc.subjectBinary optimizationen_US
dc.subjectFly Optimization Algorithmen_US
dc.subjectClassifiersen_US
dc.subjectNetworken_US
dc.titleBinary Artificial Algae Algorithm for feature selectionen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.asoc.2022.108630-
dc.identifier.scopus2-s2.0-85125840947en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume120en_US
dc.identifier.wosWOS:000791589300001en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.03. Department of Computer Engineering-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
Files in This Item:
File SizeFormat 
1-s2.0-S1568494622001211-main.pdf
  Until 2030-01-01
2.39 MBAdobe PDFView/Open    Request a copy
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

3
checked on Mar 23, 2024

WEB OF SCIENCETM
Citations

27
checked on Mar 23, 2024

Page view(s)

92
checked on Mar 25, 2024

Download(s)

6
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.