Concept-cognitive learning survey: Mining and fusing knowledge from data

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dc.contributor.authorGuo, D.
dc.contributor.authorXu, W.
dc.contributor.authorDing, W.
dc.contributor.authorYao, Y.
dc.contributor.authorWang, X.
dc.contributor.authorPedrycz, W.
dc.contributor.authorQian Y.
dc.date.accessioned2024-05-19T14:33:41Z
dc.date.available2024-05-19T14:33:41Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractConcept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in scopes, goals, and methodologies, some difficulties have gradually emerged, including some vague terminology, ambiguous views, and scattered research. Hence, a systematic and comprehensive review of the development process and advanced research about CCL is particularly necessary at the moment. This paper summarizes the theoretical significance, application value, and future development potential of CCL. More importantly, by synthesizing the reviewed related research, we can acquire some interesting results and answer three essential questions: (1) why examine a cognitive and learning framework based on concept? (2) what is the concept-cognitive learning? (3) how to make concept-cognitive learning? The findings of this work could act as a valuable guide for related studies in quest of a clear understanding of the closely related research issues around concept-cognitive learning. © 2024 Elsevier B.V.en_US
dc.description.sponsorshipNational Natural Science Foundation of China, NSFC: 62376229; Natural Science Foundation of Chongqing Municipality: CSTB2023NSCQ-LZX0027en_US
dc.description.sponsorshipThis paper is supported by the National Natural Science Foundation of China (Nos. 62376229) and Natural Science Foundation of Chongqing, China (NO. CSTB2023NSCQ-LZX0027). The authors would like to thank Editor-in-Chief, Associate Editor, and Reviewers for their insightful comments and suggestions.en_US
dc.identifier.doi10.1016/j.inffus.2024.102426
dc.identifier.issn1566-2535
dc.identifier.scopus2-s2.0-85190605901en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.inffus.2024.102426
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4308
dc.identifier.volume109en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofInformation Fusionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectConcept-Cognitive Learningen_US
dc.subjectData Miningen_US
dc.subjectGranular Computingen_US
dc.subjectInformation Fusionen_US
dc.subjectMachine Learningen_US
dc.titleConcept-cognitive learning survey: Mining and fusing knowledge from dataen_US
dc.typeArticleen_US

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