Optimal Granularity of Machine Learning Models: A Perspective of Granular Computing

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dc.contributor.authorPedrycz, Witold
dc.contributor.authorWang, Xianmin
dc.date.accessioned2024-05-19T14:46:03Z
dc.date.available2024-05-19T14:46:03Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractDesigning machine learning models followed by their deployment in a real-world environment has been an area of recent pursuits, resulting in a large number of successful applications. In particular, these applications target environments that call for a great deal of autonomy and criticality of the developed constructs and ensuing decision processes. An efficient design, carefully structured advanced architecture, high performance, and efficient learning methods are of paramount importance. Equally desired is the confidence of any result produced by the numeric model. In this study, we advocate that the associated information granularity of the numeric models and their results inherently link with the notion of specificity of information granularity. The confidence of results can be quantified in the form of an information granule where the two associated criteria of granular outcomes, such as coverage and specificity, are crucial to the holistic evaluation of the granularity of the results. It is shown that these two characteristics are conflicting and their quality becomes evaluated and optimized. Two main approaches are studied in depth. The first one concerns a granular embedding of numeric models. In the second one, we consider a synergistic environment of Gaussian process models whose results come as probabilistic information granules and can be transformed into interval information granules. An interesting architecture of a rule-based model constructed with the use of innovative clustering takes into account the generative-discriminative aspect of the process of structure discovery, which is accomplished through the optimization of some augmented objective functions. This model is investigated with regard to the two approaches to the design of the mechanism of granular assessment of results. Some illustrative examples are covered to show the essentials of the design process.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canadaen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TFUZZ.2023.3346410
dc.identifier.endpage2186en_US
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85181574134en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2176en_US
dc.identifier.urihttps://doi.org10.1109/TFUZZ.2023.3346410
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5429
dc.identifier.volume32en_US
dc.identifier.wosWOS:001196731700069en_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.subjectNumerical Modelsen_US
dc.subjectComputational Modelingen_US
dc.subjectData Modelsen_US
dc.subjectOptimizationen_US
dc.subjectGranular Computingen_US
dc.subjectProbabilistic Logicen_US
dc.subjectPrototypesen_US
dc.subjectConfidenceen_US
dc.subjectCoverageen_US
dc.subjectGaussian Process (Gp)en_US
dc.subjectGenerative-Discriminative Clusteringen_US
dc.subjectGranular Embeddingen_US
dc.subjectInformation Granulesen_US
dc.subjectSpecificityen_US
dc.titleOptimal Granularity of Machine Learning Models: A Perspective of Granular Computingen_US
dc.typeArticleen_US

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