Proposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers

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dc.contributor.authorKeles, Mumine Kaya
dc.contributor.authorKılıç, Umit
dc.contributor.authorKeles, Abdullah Emre
dc.date.accessioned2025-01-06T17:36:26Z
dc.date.available2025-01-06T17:36:26Z
dc.date.issued2021
dc.description.abstractDatasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee's Leadership Perception.
dc.description.sponsorshipAdana Alparslan Turkes Science and Technology University Scientific Research Projects Commission Unit [17103018, 18103004]
dc.description.sponsorshipAdana Alparslan Turkes Science and Technology University Scientific Research Projects Commission Unit under grant number 17103018 and 18103004.
dc.identifier.doi10.1093/comjnl/bxaa163
dc.identifier.endpage417
dc.identifier.issn0010-4620
dc.identifier.issn1460-2067
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85105505506
dc.identifier.scopusqualityQ2
dc.identifier.startpage408
dc.identifier.urihttps://doi.org/10.1093/comjnl/bxaa163
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1878
dc.identifier.volume64
dc.identifier.wosWOS:000644547300012
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofComputer Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectartifical bee colony
dc.subjectconstruction management
dc.subjectdata mining
dc.subjectfeature selection
dc.subjectinformation technology
dc.subjectleadership styles perception prediction
dc.titleProposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers
dc.typeArticle

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