Eigenclassifiers for combining correlated classifiers

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dc.authorid0000-0003-2225-7491
dc.authorid0000-0001-5838-4615
dc.authorid0000-0001-7506-0321
dc.contributor.authorUlaş, Aydınen_US
dc.contributor.authorYıldız, Olcay Taneren_US
dc.contributor.authorAlpaydın, Ahmet İbrahim Ethemen_US
dc.date.accessioned2015-01-15T23:02:04Z
dc.date.available2015-01-15T23:02:04Z
dc.date.issued2012-03-15
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ulas, M. Semerci, O.T. Yildiz, E. Alpaydin, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298-1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.en_US
dc.description.sponsorshipWe would like to thank Mehmet Gallen for discussions. This work has been supported by the Turkish Academy of Sciences in the framework of the Young Scientist Award Program (EA-TUBA-GEBIP/2001-1-1), Bogazici University Scientific Research Project 05HA101 and Turkish Scientific Technical Research Council TUBITAK EEEAG 104E079en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationUlaş, A. & Yıldız, O. T. & Alpaydın, A. İ. E. (2012). Eigenclassifiers for combining correlated classifiers. Information Sciences, 187(1), 109-120. doi:10.1016/j.ins.2011.10.024en_US
dc.identifier.doi10.1016/j.ins.2011.10.024
dc.identifier.endpage120
dc.identifier.issn0020-0255
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84155172806
dc.identifier.scopusqualityQ1
dc.identifier.startpage109
dc.identifier.urihttps://hdl.handle.net/11729/450
dc.identifier.urihttp://dx.doi.org/10.1016/j.ins.2011.10.024
dc.identifier.volume187
dc.identifier.wosWOS:000300201600007
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.subjectClassifier correlationen_US
dc.subjectClassifier design and evaluationen_US
dc.subjectMachine learningen_US
dc.subjectStacked generalizationen_US
dc.subjectPrincipal componentsen_US
dc.subjectEnsembleen_US
dc.subjectSystemsen_US
dc.subjectAlgorithmsen_US
dc.subjectBase classifiersen_US
dc.subjectData setsen_US
dc.subjectHyperparametersen_US
dc.subjectIncremental constructionen_US
dc.subjectInput featuresen_US
dc.subjectMachine-learningen_US
dc.subjectPositive correlationsen_US
dc.subjectSubset selectionen_US
dc.subjectTraining setsen_US
dc.subjectPrincipal component analysisen_US
dc.subjectClassification (of information)en_US
dc.titleEigenclassifiers for combining correlated classifiersen_US
dc.typeArticleen_US
dspace.entity.typePublication

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