A Granular Aggregation of Multifaceted Gaussian Process Models

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorYang, Lan
dc.contributor.authorZhu, Xiubin
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLi, Zhiwu
dc.contributor.authorHu, Xingchen
dc.date.accessioned2025-04-16T20:29:07Z
dc.date.available2025-04-16T20:29:07Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThis study focuses on the construction of granular Gaussian process models completed at different levels of granularity and the emergence of higher-type granular outputs through aggregating the individual prediction results. Each Gaussian process model is instantiated utilizing granular data (or information granules) to enhance algorithmic efficiency and can be tailored to specific levels of precision (granularity). The overall design methodology emphasizes human centricity in system modeling by focusing on both the interpretability and accuracy of the resulting models. First, clustering algorithms are applied to construct information granules that provide a comprehensive overview of the experimental evidence. As the number of information granules grows, the existing knowledge imbedded within data could be perceived and described at increased levels of details. Information granules are built in an augmented feature space constructed by concatenating the input and output variables. Next, Gaussian process models are constructed on a basis of the information granules formed at different levels of abstraction. Subsequently, the confidence intervals are transformed to intervals and the reconciliation of the predictions produced by individual models, which offer different perspectives on the system, leads to the emergence of more abstract entities (such as type-2 intervals/fuzzy sets, etc.) rather than plain numbers. The efficacy of the comprehensive model is measured by the coverage and specificity criteria of the granular outputs. Experimental studies conducted on a synthetic dataset and a number of real-world datasets validated the effectiveness and adaptability of the proposed methodology. © 1993-2012 IEEE.
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China under Grant Nos. 62076189, 62376279, 62302364. (Corresponding author: Xingchen Hu).
dc.identifier.citationYang, L., Zhu, X., Pedrycz, W., Li, Z., & Hu, X. (2024). A Granular Aggregation of Multifaceted Gaussian Process Models. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3464848
dc.identifier.endpage6810
dc.identifier.issn10636706
dc.identifier.issue12
dc.identifier.scopusqualityQ1
dc.identifier.startpage6801
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3464848
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6078
dc.identifier.volume32
dc.identifier.wosWOS:001371934900021
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAggregation Mechanism
dc.subjectGaussian Process Model
dc.subjectGranular Model
dc.subjectİnformation Granüle
dc.subjectPrinciple of Justifiable Granularity
dc.titleA Granular Aggregation of Multifaceted Gaussian Process Models
dc.typeArticle

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