Reinforced Interval Type-2 Fuzzy Clustering-Based Neural Network Realized Through Attention-Based Clustering Mechanism and Successive Learning

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dc.contributor.authorLiu, Shuangrong
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYang, Bo
dc.contributor.authorWang, Lin
dc.contributor.authorYoon, Jin Hee
dc.date.accessioned2024-05-19T14:39:11Z
dc.date.available2024-05-19T14:39:11Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this article, a novel attention-based reinforced interval type-2 fuzzy clustering neural network (ARIT2FCN) is developed to improve the generalization performance of fuzzy clustering-based neural networks (FCNNs). Commonly, fuzzy rules in FCNNs are generated through the clustering-based rule generator. However, the generated fuzzy rules may not be able to fully describe the given data, because the clustering-based rule generator does not simultaneously consider the intracluster homogeneity and intercluster heterogeneity for both of data characteristics and label information when defining membership functions (MFs) of fuzzy rules. This negatively affects fuzzy rules to accurately quantify the interclass heterogeneity and intraclass homogeneity and degrades the performance of FCNNs. The ARIT2FCN is proposed with the aid of the attention-based clustering mechanism and the successive learning method. The attention-based clustering mechanism is designed to define MFs by simultaneously considering data characteristics and label information. The successive learning method is adopted to construct the desired fuzzy rules that can capture the interclass heterogeneity and intraclass homogeneity. Moreover, L-2 norm regularization is used to alleviate the overfitting effect. The performance of ARIT2FCN is evaluated on machine learning datasets with 16 comparative methods. In addition, two real-world problems are adopted to validate the effectiveness of ARIT2FCN. Experimental results demonstrate that the ARIT2FCN outperforms the comparative methods, and the statistical tests also support the superiority of ARIT2FCN.en_US
dc.description.sponsorshipNational Research Foundation of Koreaen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TFUZZ.2023.3321197
dc.identifier.endpage1222en_US
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85174805009en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1208en_US
dc.identifier.urihttps://doi.org10.1109/TFUZZ.2023.3321197
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4721
dc.identifier.volume32en_US
dc.identifier.wosWOS:001179721500061en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAttention-Based Clustering Mechanismen_US
dc.subjectFuzzy Clustering-Based Neural Network (Fcnn)en_US
dc.subjectInterval Type-2 Fuzzy C-Meansen_US
dc.subjectL-2 Norm Regularizationen_US
dc.subjectSuccessive Learningen_US
dc.titleReinforced Interval Type-2 Fuzzy Clustering-Based Neural Network Realized Through Attention-Based Clustering Mechanism and Successive Learningen_US
dc.typeArticleen_US

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