Linguistic Models: Optimization With the Use of Conditional Fuzzy C-Means

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dc.authoridZhu, Xiubin/0000-0002-7947-8749
dc.authorwosidSucci, Giancarlo/E-4064-2016
dc.contributor.authorJing, TaiLong
dc.contributor.authorPedrycz, Witold
dc.contributor.authorZhu, XiuBin
dc.contributor.authorSucci, Giancarlo
dc.contributor.authorLi, ZhiWu
dc.date.accessioned2024-05-19T14:40:21Z
dc.date.available2024-05-19T14:40:21Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractMost fuzzy models are just numeric. In this study, we revisit, explore and augment a concept of linguistic models, viz., fuzzy models producing results that are information granules, and, specifically, intervals or fuzzy sets. The proposed architecture is formed by constructing a network of linked fuzzy sets (information granules) ininput and output spaces with the aid of a context-based Fuzzy C-Means clustering method. The user centricity of such clustering method is implied by the explicit formulation of fuzzy sets in the output space. The resulting information granules constructed in the input space are conditioned by the corresponding fuzzy sets in the output space. This arrangement can increase the interpretability of the model and represent the model as a collection of logically arranged associations among information granules. The model's overall design process is discussed along with a detailed algorithmic structure. Its experimental evaluations are provided by using both synthetic and publicly datasets. For the former, the model brings the performance improvement ranging from 91% to 250% over the models with information granules uniformly distributed in output space. For the latter, such improvement ranges from 6% to 94%. Finally, a thorough discussion is provided together with guidelines on how to develop such a linguistic model in different contexts.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62076189]en_US
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China under Grant 62076189.en_US
dc.identifier.doi10.1109/TETCI.2023.3265391
dc.identifier.endpage1141en_US
dc.identifier.issn2471-285X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85159719607en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1136en_US
dc.identifier.urihttps://doi.org10.1109/TETCI.2023.3265391
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4946
dc.identifier.volume8en_US
dc.identifier.wosWOS:000986644100001en_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 Emerging Topics In Computational Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectComputational Modelingen_US
dc.subjectLinguisticsen_US
dc.subjectFuzzy Setsen_US
dc.subjectModelingen_US
dc.subjectContext Modelingen_US
dc.subjectData Modelsen_US
dc.subjectComputer Architectureen_US
dc.subjectLinguistic Modelen_US
dc.subjectRule-Based Architectureen_US
dc.subjectConditional Fuzzy C-Meansen_US
dc.subjectInterpretabilityen_US
dc.subjectCoverage And Specificityen_US
dc.titleLinguistic Models: Optimization With the Use of Conditional Fuzzy C-Meansen_US
dc.typeArticleen_US

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