Artificial immune recognition system based classifier ensemble on the different feature subsets for detecting the cardiac disorders from SPECT images

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dc.contributor.authorPolat, Kemal
dc.contributor.authorSekerci, Ramazan
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:17:01Z
dc.date.available2020-03-26T17:17:01Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description18th International Conference on Database and Expert Systems Applications -- SEP 03-07, 2007 -- Univ Regensburg, Regensburg, GERMANYen_US
dc.description.abstractCombining outputs of multiple classifiers is one of most important techniques for improving classification accuracy. In this paper, we present a new classifier ensemble based on artificial immune recognition system (AIRS) classifier and independent component analysis (ICA) for detecting the cardiac disorders from SPECT images. Firstly, the dimension of SPECT (Single Photon Emission Computed Tomography) images dataset, which has 22 binary features, was reduced to 3, 4, and 5 features using FastICA algorithm. Three different feature subsets were obtained in this way. Secondly, the obtained feature subsets were classified by AIRS classifier and then stored the outputs obtained from AIRS classifier into the result matrix. The exact result that denote whether subject has cardiac disorder or not was obtained by averaging the outputs obtained from AIRS classifier into the result matrix. While only AIRS classifier obtained 84.96% classification accuracy with 50-50% train-test split for diagnosing the cardiac disorder from SPECT images, classifier ensemble based on AIRS and ICA fusion obtained 97.74% classification accuracy on the same conditions. The accuracy of AIRS classifier utilizing the reduced feature subsets was higher than those exploiting all the original features. These results show that the proposed ensemble method is very promising in diagnosis of the cardiac disorder from SPECT images.en_US
dc.description.sponsorshipDEXA Assoc, Austrian Comp Soc, Res Inst Appl Knowledge Proc, Univ Regensburgen_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Selcuk University.en_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-3-540-74467-2
dc.identifier.issn0302-9743en_US
dc.identifier.issn1611-3349en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage45en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21223
dc.identifier.volume4653en_US
dc.identifier.wosWOS:000250750100005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofDATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGSen_US
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleArtificial immune recognition system based classifier ensemble on the different feature subsets for detecting the cardiac disorders from SPECT imagesen_US
dc.typeConference Objecten_US

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