Improving classification accuracy with discretization on datasets including continuous valued features

Basit Öğe Kaydı
dc.contributor.authorHacibeyoglu M.
dc.contributor.authorArslan A.
dc.contributor.authorKahramanli S.
dc.date.accessioned2020-03-26T18:22:15Z
dc.date.available2020-03-26T18:22:15Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study analyzes the effect of discretization on classification of datasets including continuous valued features. Six datasets from UCI which containing continuous valued features are discretized with entropy-based discretization method. The performance improvement between the dataset with original features and the dataset with discretized features is compared with k-nearest neighbors, Naive Bayes, C4.5 and CN2 data mining classification algorithms. As the result the classification accuracies of the six datasets are improved averagely by 1.71% to 12.31%.en_US
dc.identifier.endpage558en_US
dc.identifier.issn2010376Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage555en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27245
dc.identifier.volume78en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofWorld Academy of Science, Engineering and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectData mining classification algorithmsen_US
dc.subjectEntropy-based discretization methoden_US
dc.titleImproving classification accuracy with discretization on datasets including continuous valued featuresen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu