A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN)

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dc.contributor.authorCeylan, Rahime
dc.contributor.authorKoyuncu, Hasan
dc.date.accessioned2020-03-26T20:12:22Z
dc.date.available2020-03-26T20:12:22Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractNeural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets. (C) 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.en_US
dc.identifier.doi10.1016/j.jksuci.2017.10.011en_US
dc.identifier.endpage251en_US
dc.identifier.issn1319-1578en_US
dc.identifier.issn2213-1248en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage235en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.jksuci.2017.10.011
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37434
dc.identifier.volume31en_US
dc.identifier.wosWOS:000462516600009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.ispartofJOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectRotation Foresten_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectArtificial Bee Colony Optimizationen_US
dc.subjectScout Particle Swarm Optimizationen_US
dc.subjectHybrid classifiersen_US
dc.titleA Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN)en_US
dc.typeArticleen_US

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