Feature data-driven-reinforced fuzzy radial basis function neural network classifier with the aid of preprocessing techniques and particle swarm optimization

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dc.contributor.authorPark, Sang-Beom
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:46:28Z
dc.date.available2024-05-19T14:46:28Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this study, reinforced fuzzy radial basis function neural networks (FRBFNN) classifier driven by feature extracted data completed with the aid of effectively preprocessing techniques and evolutionary optimization, and its comprehensive design methodology are introduced. An Overall structure of the reinforced FRBFNN comprises the preprocessing part, the premise part and the consequence part of fuzzy rules of the network. In the preprocessing part, four types of preprocessing algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), combination of PCA and LDA (Hybrid PCA) and fuzzy transform are considered. To extract feature data suitable to characterize signal data, the feature extraction of information data is carried out through the dimensionality reduction done by the preprocessing technique, and then the reduced data are used as the input to the FRBFNN classifier. In the premise part of fuzzy rules of the network, the number of fuzzy rules is determined according to the number of clusters by fuzzy c-means (FCM) clustering. The fitness values of individual fuzzy rules are obtained based on data distribution. In the consequence part of fuzzy rules of the network, the parameters of connection weights located between the hidden layer and the output layer of FRBFNN classifier are estimated by means of the least square estimation. Particle swarm optimization (PSO) is exploited for structural as well as parametric optimization in the FRBFNN classifier. The parameters to be optimized by PSO are related to six factors such as the determination of whether to use data preprocessing, the type of data preprocessing technique, the number of input variables reduced by the preprocessing technique, fuzzification coefficient and the number of fuzzy rules used in fuzzy c-means (FCM) clustering, and the type of connection weights. By using diverse benchmark dataset obtained from UCI repository, the classification performance of the reinforced FRBFNN classifier was evaluated. Through a variety of classification algorithms existed in the Weka data mining software (Weka), the classification performance of the reinforced FRBFNN classifier was compared as well. The superiority of the proposed classifier is demonstrated through Friedman test. Furthermore, we assessed the classification performance of the reinforced FRBFNN classifier applied to black plastic wastes spectral data acquired from Raman and Laser induced breakdown spectroscopy equipment for the practical application of the material sorting system of the black plastic wastes.en_US
dc.description.sponsorshipNational Research Foundation of Korea (NRF) - Korea Government (MSIT) [NRF-2021R1F1A1056102]en_US
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2021R1F1A1056102).en_US
dc.identifier.doi10.1007/s00500-023-09124-6
dc.identifier.endpage15462en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue21en_US
dc.identifier.scopus2-s2.0-85168616201en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage15443en_US
dc.identifier.urihttps://doi.org10.1007/s00500-023-09124-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5529
dc.identifier.volume27en_US
dc.identifier.wosWOS:001060086300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectReinforced Fuzzy Radial Basis Function Neural Networks (Frbfnn)en_US
dc.subjectPreprocessing Techniquesen_US
dc.subjectParticle Swarm Optimization (Pso)en_US
dc.subjectPractical Applicationen_US
dc.subjectBlack Plastic Wastesen_US
dc.titleFeature data-driven-reinforced fuzzy radial basis function neural network classifier with the aid of preprocessing techniques and particle swarm optimizationen_US
dc.typeArticleen_US

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