Tree Ensembles on the induced discrete space

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dc.authorid0000-0001-5838-4615
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2016-05-23T12:21:12Z
dc.date.available2016-05-23T12:21:12Z
dc.date.issued2016-05
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractDecision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and these orderings are used as the new attributes in decision tree induction. Although K-tree performs significantly better than the proper one, their exponential time complexity can prohibit their use. In this brief, we propose K-forest, an extension of random forest, where a subset of features is selected randomly from the induced discrete space. Simulation results on 17 data sets show that the novel ensemble classifier has significantly lower error rate compared with the random forest based on the original feature space.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationYıldız, O. T. (2016). Tree ensembles on the induced discrete space. IEEE Transactions on Neural Networks and Learning Systems, 27(5), 1108-1113. doi:10.1109/TNNLS.2015.2430277en_US
dc.identifier.doi10.1109/TNNLS.2015.2430277
dc.identifier.endpage1113
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.issue5
dc.identifier.pmid26011897
dc.identifier.scopus2-s2.0-84929773889
dc.identifier.scopusqualityQ1
dc.identifier.startpage1108
dc.identifier.urihttps://hdl.handle.net/11729/863
dc.identifier.urihttp://dx.doi.org/10.1109/TNNLS.2015.2430277
dc.identifier.volume27
dc.identifier.wosWOS:000375113700015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.journalIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectDecision treesen_US
dc.subjectFeature extractionen_US
dc.subjectRandom foresten_US
dc.subjectArtificial intelligenceen_US
dc.subjectClassification (of information)en_US
dc.subjectLearning systemen_US
dc.subjectDecision tree inductionen_US
dc.subjectDiscrete attributesen_US
dc.subjectDiscrete spacesen_US
dc.subjectEnsemble classifiersen_US
dc.subjectExponential time complexityen_US
dc.subjectPredictive modelsen_US
dc.subjectTree ensemblesen_US
dc.titleTree Ensembles on the induced discrete spaceen_US
dc.typeArticleen_US
dspace.entity.typePublication

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