Dual-Channel Fuzzy Interaction Information Fused Feature Selection With Fuzzy Sparse and Shared Granularities

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dc.authoridHengrong Ju
dc.authoridXiaoxue Fan / 0009-0002-8977-1604
dc.authoridWeiping Ding / 0000-0002-3331-1011
dc.authoridJiashuang Huang
dc.authoridSuping Xu
dc.authoridXibei Yang
dc.authoridWitold Pedrycz / 0000-0002-9335-9930
dc.authorwosidWitold Pedrycz / LOC-1073-2024
dc.contributor.authorJu, Hengrong
dc.contributor.authorFan, Xiaoxue
dc.contributor.authorDing, Weiping
dc.contributor.authorHuang, Jiashuang
dc.contributor.authorXu, Suping
dc.contributor.authorYang, Xibei
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-18T09:26:02Z
dc.date.available2025-04-18T09:26:02Z
dc.date.issued14.11.2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractFuzzy information granularity is an effective granular computation approach for feature evaluation and selection. However, most existing methods rely on a single granulation channel, neglecting different granularity representations. In this article, a novel dual-channel fuzzy interaction information fused feature selection with fuzzy sparse and shared granularities is proposed. It mainly comprises the following three parts. First, a dual-channel framework is introduced to construct the fuzzy information granularity from two different strategies. One channel employs sparse mutual strategy to form the sparse representation-based fuzzy information granularity, while the other constructs the fuzzy shared information granularity with a novel fuzzy semi-ball. Second, in each channel, the criteria of maximum relevancy, minimum redundancy, and maximum interaction is adopted to access feature correlation and perform feature ranking. Third, the two feature sequences derived from the dual-channel are fused to form a final feature sequence based on the within-class and between-class mechanism. To validate the efficacy of the proposed method, experimental validations on 15 datasets and schizophrenia data are conducted. The results show that the proposed method outperforms other algorithms in classification accuracy and statistical analysis. Moreover, its superiority regarding accuracy can be demonstrated in the experiments of schizophrenia detection, where it performs well in recognizing schizophrenia through visual interpretation.
dc.identifier.citationJu, H., Fan, X., Ding, W., Huang, J., Xu, S., Yang, X., & Pedrycz, W. (2024). Dual-channel fuzzy interaction information fused feature selection with fuzzy sparse and shared granularities. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3438364
dc.identifier.endpage6068
dc.identifier.issn1063-6706
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85200816370
dc.identifier.scopusqualityQ1
dc.identifier.startpage6056
dc.identifier.urihttps://www.webofscience.com/wos/woscc/full-record/WOS:001348284400042
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6790
dc.identifier.volume32
dc.identifier.wosWOS:001348284400042
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 INC
dc.relation.ispartofIEEE TRANSACTIONS ON FUZZY SYSTEMS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectfeature measurefeature selectionDual-channelfuzzy information granularityfuzzy information granularityfeature selectionfuzzy information granularity
dc.titleDual-Channel Fuzzy Interaction Information Fused Feature Selection With Fuzzy Sparse and Shared Granularities
dc.typeArticle

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