Takagi-sugeno-kang fuzzy systems for high-dimensional multilabel classification

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / FPE-7309-2022
dc.contributor.authorBian, Ziwei
dc.contributor.authorChang, Qin
dc.contributor.authorWang, Jian
dc.contributor.authorPedrycz, Witold
dc.contributor.authorPal, Nikhil R.
dc.date.accessioned2025-04-18T10:11:00Z
dc.date.available2025-04-18T10:11:00Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMultilabel classification (MLC) refers to associating each instance with multiple labels simultaneously. MLC has gained much importance due to its ability to better reflect the complexity of the real world classification problems. Fuzzy system (FS) has excellent nonlinear modeling capability and strong interpretability, which makes it a promising model for complex MLC problems. However, it is widely known that FS suffers from the "curse of dimensionality." Here, an adaptive membership function (MF) along with its generalized version is proposed to address high-dimensional problems. These MFs can effectively overcome "numeric underflow" in FS while preserving interpretability as much as possible. On this basis, a novel fuzzy rule based MLC framework called multilabel high-dimensional Takagi-Sugeno-Kang fuzzy system (ML-HDTSK FS) is proposed. This model can handle data with over ten thousand dimensionality. In addition, ML-HDTSK FS uses a decomposed label correlation learning strategy to efficiently capture both high and low levels of relationship between labels, and adopts a group L21 penalty to realize the learning of label-specific features. Combining these two new multilabel learning strategies and the novel adaptive MF, ML-HDTSK FS becomes a more powerful tool for various MLC problems. The effectiveness of ML-HDTSK FS is demonstrated on seventeen benchmark multilabel datasets, and its performance is compared with eleven MLC algorithms. The experimental results confirm the validity of the proposed ML-HDTSK FS, and demonstrate the superiority of it in dealing with MLC problems, especially for high dimensional ones.
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC)
dc.identifier.citationBian, Z., Chang, Q., Wang, J., Pedrycz, W., & Pal, N. R. (2024). Takagi-Sugeno-Kang Fuzzy Systems for High-Dimensional Multi-Label Classification. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3382981
dc.identifier.endpage3804
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85189643656
dc.identifier.scopusqualityQ1
dc.identifier.startpage3790
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3382981
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6981
dc.identifier.volume32
dc.identifier.wosWOS:001240137400022
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.subjectCorrelation
dc.subjectFuzzy Systems
dc.subjectRepresentation Learning
dc.subjectFiring
dc.subjectTakagi-Sugeno Model
dc.subjectClassification Algorithms
dc.subjectAdaptation Models
dc.subjectHigh-Dimensional Data
dc.subjectLabel Correlation Lear Ning
dc.subjectLabel-Specific Feature Learning
dc.subjectMultilabel Classification (MLC)
dc.subjectTakagi-Sugeno-Kang Fuzzy Systems
dc.titleTakagi-sugeno-kang fuzzy systems for high-dimensional multilabel classification
dc.typeArticle

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