Feature extraction from discrete attributes

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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 extract new features by combining k discrete attributes, where for each subset of size k of the attributes, we generate all orderings of values of those attributes exhaustively. We then apply the usual univariate decision tree classifier using these orderings as the new attributes. Our simulation results on 16 datasets from UCI repository [2] show that the novel decision tree classifier performs better than the proper in terms of error rate and tree complexity. The same idea can also be applied to other univariate rule learning algorithms such as C4.5Rules [7] and Ripper [3].en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationYıldız, O. T. (2010). Feature extraction from discrete attributes. Paper presented at the Proceedings - International Conference on Pattern Recognition, 3915-3918. doi:10.1109/ICPR.2010.952en_US
dc.identifier.doi10.1109/ICPR.2010.952
dc.identifier.endpage3918
dc.identifier.isbn9780769541099
dc.identifier.issn1051-4651
dc.identifier.scopus2-s2.0-78149471942
dc.identifier.scopusqualityN/A
dc.identifier.startpage3915
dc.identifier.urihttps://hdl.handle.net/11729/1959
dc.identifier.urihttps://dx.doi.org/10.1109/ICPR.2010.952
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartofProceedings - International Conference on Pattern Recognitionen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData setsen_US
dc.subjectDecision tree classifiersen_US
dc.subjectDiscrete attributesen_US
dc.subjectError rateen_US
dc.subjectRule learning algorithmsen_US
dc.subjectSimulation resulten_US
dc.subjectTree complexityen_US
dc.subjectUCI repositoryen_US
dc.subjectUnivariateen_US
dc.subjectClassifiersen_US
dc.subjectDecision treesen_US
dc.subjectLearning algorithmsen_US
dc.subjectFeature extractionen_US
dc.subjectError analysisen_US
dc.subjectTrainingen_US
dc.subjectImpuritiesen_US
dc.subjectPattern recognitionen_US
dc.subjectPrincipal component analysisen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectPattern classificationen_US
dc.subjectK discrete attributesen_US
dc.subjectUnivariate decision tree classifieren_US
dc.subjectUnivariate rule learning algorithmsen_US
dc.subjectC4.5 Rulesen_US
dc.subjectRipperen_US
dc.titleFeature extraction from discrete attributesen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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