Artificial Neural Network Based on Rotation Forest for Biomedical Pattern Classification

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dc.contributor.authorKoyuncu, Hasan
dc.contributor.authorCeylan, Rahime
dc.date.accessioned2020-03-26T18:41:09Z
dc.date.available2020-03-26T18:41:09Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description36th International Conference on Telecommunications and Signal Processing (TSP) -- JUL 02-04, 2013 -- Rome, ITALYen_US
dc.description.abstractThe novel classifier system based on ensemble classifier is proposed in this paper. Rotation forest algorithm based on principal component algorithm was used as ensemble classifier method. In presented classifier system, artificial neural network was used as base classifier in this ensemble classifier system. Rotation forest structure has been generally realized with decision trees in literature. But, multilayer perceptron neural network was utilized as base classifier in rotation forest structure in our study. However, principal component analysis was used for obtaining different feature sets from original data set. The proposed RF-ANN structure was applied to Wisconsin breast cancer data taken form UCI Database. The obtained results were compared with the results of neural network optimized particle swarm optimization (PSO-ANN). The realized experimental studies were represented that RF-ANN structure was successful than PSO-ANN structure. RF-ANN classified breast cancer dataset with 98.05% classification accuracy using 9 classifiers.en_US
dc.description.sponsorshipIEEE, Czechoslovakia Sect, Investment & Business Dev Agcy Czech Republ, Brno Univ Technol, Dept Telecommunicat, Budapest Univ Technol & Econ, Dept Telecommunicat, Karadeniz Tech Univ, Dept Elect & Elect Engn, W Pomeranian Univ Technol, Fac Elect Engn, Tech Univ Ostrava, Dept Telecommunicat, Slovak Univ Technol, Inst Telecommunicat, Univ Ljubljana, Lab Telecommunicat, Czech Tech Univ, Dept Telecommunicat Engn, Adv Elect & Elect Engn Journal, Int Journal Adv Telecommunicat, Electrotechn, Signals & Systen_US
dc.identifier.endpage585en_US
dc.identifier.isbn978-1-4799-0402-0; 978-1-4799-0403-7
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage581en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29241
dc.identifier.wosWOS:000333968000118en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2013 36TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectbiomedical pattern classificationen_US
dc.subjectclassifier ensemblesen_US
dc.subjectrotation foresten_US
dc.titleArtificial Neural Network Based on Rotation Forest for Biomedical Pattern Classificationen_US
dc.typeConference Objecten_US

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