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dc.contributor.authorKilimci, Zeynep
dc.contributor.authorOmurca, Sevinc
dc.date.accessioned2021-06-14T20:25:01Z
dc.date.available2021-06-14T20:25:01Z
dc.date.issued2020
dc.identifier.issn1683-3198
dc.identifier.urihttps://doi.org/10.34028/iajit/17/2/6
dc.identifier.urihttps://hdl.handle.net/11376/3664
dc.descriptionKilimci, Zeynep Hilal/0000-0003-1497-305Xen_US
dc.description.abstractExtended space forests are a matter of common knowledge for ensuring improvements on classification problems. They provide richer feature space and present better performance than the original feature space-based forests. Most of the contemporary studies employs original features as well as various combinations of them as input vectors for extended space forest approach. In this study, we seek to boost the performance of classifier ensembles by integrating them with heuristic optimization-based features. The contributions of this paper are fivefold. First, richer feature space is developed by using random combinations of input vectors and features picked out with ant colony optimization method which have high importance and not have been associated before. Second, we propose widely used classification algorithm which is utilized baseline classifier. Third, three ensemble strategies, namely bagging, random subspace, and random forests are proposed to ensure diversity. Fourth, a wide range of comparative experiments are conducted on widely used biomedicine datasets gathered from the University of California Irvine (UCI) machine learning repository to contribute to the advancement of proposed study. Finally, extended space forest approach with the proposed technique turns out remarkable experimental results compared to the original version and various extended versions of recent state-of-art studies.en_US
dc.language.isoengen_US
dc.publisherZarka Private Univen_US
dc.identifier.doi10.34028/iajit/17/2/6en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassifier ensemblesen_US
dc.subjectextended space forestsen_US
dc.subjectant colony optimizationen_US
dc.subjectdecision treeen_US
dc.titleEnhancement of the Heuristic Optimization Based on Extended Space Forests using Classifier Ensemblesen_US
dc.typearticleen_US
dc.relation.journalInternational Arab Journal Of Information Technologyen_US
dc.department[0-Belirlenecek]en_US
dc.identifier.volume17en_US
dc.identifier.issue2en_US
dc.identifier.startpage188en_US
dc.identifier.endpage195en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.institutionauthor[0-Belirlenecek]
dc.department-temp[Kilimci, Zeynep] Dogus Univ, Dept Comp Engn, Dogus, Turkey; [Omurca, Sevinc] Kocaeli Univ, Dept Comp Engn, Kocaeli, Turkey; [Kilimci, Zeynep] Kocaeli Univ, Dept Informat Syst Engn, Kocaeli, Turkeyen_US
dc.identifier.wosWOS:000528659200006en_US


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