Model selection in omnivariate decision trees using Structural Risk Minimization

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dc.authorid0000-0001-5838-4615
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2015-01-15T23:01:51Z
dc.date.available2015-01-15T23:01:51Z
dc.date.issued2011-12-01
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.descriptionThe authors thank the three anonymous referees and the editor for their constructive comments, pointers to related literature, and pertinent questions which allowed us to better situate our work as well as organize the ms and improve the presentation. This work has been supported by the Turkish Scientific Technical Research Council TUBITAK EEEAG 107E127en_US
dc.description.abstractAs opposed to trees that use a single type of decision node, an omnivariate decision tree contains nodes of different types. We propose to use Structural Risk Minimization (SRM) to choose between node types in omnivariate decision tree construction to match the complexity of a node to the complexity of the data reaching that node. In order to apply SRM for model selection, one needs the VC-dimension of the candidate models. In this paper, we first derive the VC-dimension of the univariate model, and estimate the VC-dimension of all three models (univariate, linear multivariate or quadratic multivariate) experimentally. Second, we compare SRM with other model selection techniques including Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC) and cross-validation (CV) on standard datasets from the UCI and Delve repositories. We see that SRM induces omnivariate trees that have a small percentage of multivariate nodes close to the root and they generalize more or at least as accurately as those constructed using other model selection techniques.en_US
dc.description.sponsorshipTÜBİTAKen_US
dc.description.versionPublisher's Versionen_US
dc.description.versionAuthor Pre-Printen_US
dc.identifier.citationYıldız, O. T. (2011). Model selection in omnivariate decision trees using structural risk minimization. Information Sciences, 181(23), 5214-5226. doi:10.1016/j.ins.2011.07.028en_US
dc.identifier.doi10.1016/j.ins.2011.07.028
dc.identifier.endpage5226
dc.identifier.issn0020-0255
dc.identifier.issn1872-6291
dc.identifier.issue23
dc.identifier.scopus2-s2.0-80052917492
dc.identifier.scopusqualityQ1
dc.identifier.startpage5214
dc.identifier.urihttps://hdl.handle.net/11729/400
dc.identifier.urihttp://dx.doi.org/10.1016/j.ins.2011.07.028
dc.identifier.volume181
dc.identifier.wosWOS:000295760600007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_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.publisherElsevier Science Incen_US
dc.relation.ispartofInformation Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectMachine learningen_US
dc.subjectModel selectionen_US
dc.subjectVC-dimensionen_US
dc.subjectStructural risk minimizationen_US
dc.subjectDecision treeen_US
dc.titleModel selection in omnivariate decision trees using Structural Risk Minimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication

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