SCINN: semantic concept-based inference neural networks with explainable and deep fuzzy structure

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorLiu, Shuangrong
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYang, Bo
dc.contributor.authorWang, Lin
dc.contributor.authorPeng, Zhen
dc.date.accessioned2025-04-18T10:24:29Z
dc.date.available2025-04-18T10:24:29Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn this study, a novel semantic concept-based inference neural network (SCINN) is proposed to develop a design methodology for the explainable deep neuro-fuzzy models and improve their generalization performance in high-dimensional problems. Traditional neuro-fuzzy models exhibit outstanding interpretability in the problems of lower dimensionality. However, when faced with high-dimensional scenarios, the long rule and rule explosion problems damage their interpretability and result in poor generalization performance, even making them unusable. Although deep neuro-fuzzy models show enhanced performance in handling high-dimensional problems compared to traditional counterparts, they often come at the expense of interpretability. To establish a neuro-fuzzy model that can address high-dimensional problems while preserving the interpretability, the SCINN is proposed with the aid of the concept-based measure generation paradigm (CMGP) and the multi-view information augmentation strategy (MIAS). The CMGP is designed to adaptively define the membership functions (MFs) that correspond to the human-understandable concepts based on the given data; the defined MFs contribute to the construction of the explainable fuzzy rule that can directly process high-dimensional data. The MIAS is structured to develop a unified paradigm for implementing consequence functions in the fuzzy rules, which enhances the approximation ability of the SCINN. The performance of SCINN is evaluated on various image datasets against different competitors, including neuro-fuzzy-based approaches and deep structure-based neural networks. A real-world application is adopted to evaluate its effectiveness. The experimental results show that SCINN outperforms the compared neuro-fuzzy models and is comparable to the deep structure-based neural networks.
dc.description.sponsorshipNational Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea International (Regional) Cooperation and Exchange (ICE) Projects of the National Natural Science Foundation of China (NSFC) National Natural Science Foundation of China (NSFC) Natural Science Foundation of Shandong Province The "New 20 Rules for University" Program of Jinan City
dc.identifier.citationLiu, S., Oh, S. K., Pedrycz, W., Yang, B., Wang, L., & Peng, Z. (2024). SCINN: Semantic Concept-Based Inference Neural Networks With Explainable and Deep Fuzzy Structure. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3398719
dc.identifier.endpage4147
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85192777364
dc.identifier.scopusqualityQ1
dc.identifier.startpage4133
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3398719
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7053
dc.identifier.volume32
dc.identifier.wosWOS:001263598700016
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE-inst electrical electronics engineers
dc.relation.ispartofIEEE transactions on fuzzy systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFuzzy Inference Framework
dc.subjectGenerative Adversarial Network (GAN)
dc.subjectInterpretability
dc.subjectMultiview
dc.subjectInformation Augmentation
dc.subjectSemantic Concept-Based Inference
dc.titleSCINN: semantic concept-based inference neural networks with explainable and deep fuzzy structure
dc.typeArticle

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