An examination on the effect of CVNN parameters while classifying the real-valued balanced and unbalanced data

dc.contributor.authorAcar, Yunus Emre
dc.contributor.authorCeylan, Murat
dc.contributor.authorYaldiz, Ercan
dc.date.accessioned2020-03-26T19:52:51Z
dc.date.available2020-03-26T19:52:51Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractIn this study, a Complex-Valued Neural Network is designed to investigate the effects of the mapping angle and the learning rate on both imbalanced and balanced data. Symmetry detection problems with 3 different lengths are handled as the imbalanced data with event rates of 0.25, 0.125 and 0.0675. In order to make the data balanced, the symmetric members of the training set are resampled. The effects of the learning rate and the mapping angle are investigated for 3 different activation functions. The performance of the CVNN is measured using confusion matrix. 4-fold cross validation is used to validate the results. The results show that the CVNN is a strong tool to classify both the real valued imbalanced and balanced data with the right mapping angle and the learning rate that suit the selected activation function.en_US
dc.description.sponsorshipInonu Univ, Comp Sci Dept, IEEE Turkey Sect, Anatolian Scien_US
dc.identifier.isbn978-1-5386-6878-8
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36315
dc.identifier.wosWOS:000458717400184en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleAn examination on the effect of CVNN parameters while classifying the real-valued balanced and unbalanced dataen_US
dc.typeConference Objecten_US

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