Univariate margin tree

dc.authorid0000-0001-5838-4615
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2019-08-31T12:10:23Z
dc.date.accessioned2019-08-05T16:04:59Z
dc.date.available2019-08-31T12:10:23Z
dc.date.available2019-08-05T16:04:59Z
dc.date.issued2010
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree, where for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 datasets show that the novel margin tree classifier performs at least as good as C4.5 and LDT with a similar time complexity. For two class datasets it generates smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass datasets with one-vs-rest methodology.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationYıldız, O. T. (2010). Univariate margin tree. Paper presented at the Lecture Notes in Electrical Engineering, 62, 11-16. doi:10.1007/978-90-481-9794-1_3en_US
dc.identifier.doi10.1007/978-90-481-9794-1_3
dc.identifier.endpage16
dc.identifier.isbn9789048197934
dc.identifier.isbn9048197937
dc.identifier.issn1876-1100
dc.identifier.scopus2-s2.0-78651566091
dc.identifier.scopusqualityQ4
dc.identifier.startpage11
dc.identifier.urihttps://hdl.handle.net/11729/1960
dc.identifier.urihttps://dx.doi.org/10.1007/978-90-481-9794-1_3
dc.identifier.volume62
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringeren_US
dc.relation.ispartofLecture Notes in Electrical Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectContinuous attributeen_US
dc.subjectConvex optimizationen_US
dc.subjectData setsen_US
dc.subjectDecision treesen_US
dc.subjectDecision tree learning algorithmen_US
dc.subjectInformation scienceen_US
dc.subjectLearning algorithmsen_US
dc.subjectMulti-classen_US
dc.subjectNeural networksen_US
dc.subjectPattern recognitionen_US
dc.subjectSimulation resulten_US
dc.subjectSupport vector machinesen_US
dc.subjectTime complexityen_US
dc.subjectTree classifiersen_US
dc.subjectUnivariateen_US
dc.titleUnivariate margin treeen_US
dc.typeConference Objecten_US

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