Omnivariate rule induction using a novel pairwise statistical test

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dc.authorid0000-0001-5838-4615
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2015-01-15T23:02:16Z
dc.date.available2015-01-15T23:02:16Z
dc.date.issued2013-09
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractRule learning algorithms, for example, RIPPER, induces univariate rules, that is, a propositional condition in a rule uses only one feature. In this paper, we propose an omnivariate induction of rules where under each condition, both a univariate and a multivariate condition are trained, and the best is chosen according to a novel statistical test. This paper has three main contributions: First, we propose a novel statistical test, the combined 5 x 2 cv t test, to compare two classifiers, which is a variant of the 5 x 2 cv t test and give the connections to other tests as 5 x 2 cv F test and k-fold paired t test. Second, we propose a multivariate version of RIPPER, where support vector machine with linear kernel is used to find multivariate linear conditions. Third, we propose an omnivariate version of RIPPER, where the model selection is done via the combined 5 x 2 cv t test. Our results indicate that 1) the combined 5 x 2 cv t test has higher power (lower type II error), lower type I error, and higher replicability compared to the 5 x 2 cv t test, 2) omnivariate rules are better in that they choose whichever condition is more accurate, selecting the right model automatically and separately for each condition in a rule.en_US
dc.description.versionPublisher's Versionen_US
dc.description.versionAuthor Post Printen_US
dc.identifier.citationYıldız, O. T. (2013). Omnivariate rule induction using a novel pairwise statistical test. IEEE Transactions on Knowledge and Data Engineering, 25(9), 2105-2118. doi:10.1109/TKDE.2012.155en_US
dc.identifier.doi10.1109/TKDE.2012.155
dc.identifier.endpage2118
dc.identifier.issn1041-4347
dc.identifier.issn1558-2191
dc.identifier.issue9
dc.identifier.scopus2-s2.0-84881044423
dc.identifier.scopusqualityQ1
dc.identifier.startpage2105
dc.identifier.urihttps://hdl.handle.net/11729/480
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2012.155
dc.identifier.volume25
dc.identifier.wosWOS:000322136900013
dc.identifier.wosqualityQ1
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.publisherIEEE Computer Socen_US
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRule inductionen_US
dc.subjectModel selectionen_US
dc.subjectStatistical testsen_US
dc.subjectSupport vector machinesen_US
dc.subjectAnt colony optimizationen_US
dc.subjectClassification treesen_US
dc.subjectLearning algorithmsen_US
dc.subjectDecision treesen_US
dc.titleOmnivariate rule induction using a novel pairwise statistical testen_US
dc.typeArticleen_US
dspace.entity.typePublication

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