Academic Journal
A multi-view TSK fuzzy system with deformable Gaussian membership functions and rule-level attention for classification.
| Τίτλος: | A multi-view TSK fuzzy system with deformable Gaussian membership functions and rule-level attention for classification. |
|---|---|
| Συγγραφείς: | Huang Z; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.; Engineering Research Center of the Ministry of Education for Intelligent Technology and Healthcare, Jiangnan University, P.R. China., Jiang Y; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.; Engineering Research Center of the Ministry of Education for Intelligent Technology and Healthcare, Jiangnan University, P.R. China., Xia K; Engineering Research Center of the Ministry of Education for Intelligent Technology and Healthcare, Jiangnan University, P.R. China.; Changshu Key Laboratory of Medical Affiliated Intelligence and Big Data, Suzhou, Jiangsu, China.; Center of Intelligent Medical Technology Research, Changshu Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China. |
| Πηγή: | PloS one [PLoS One] 2026 May 11; Vol. 21 (5), pp. e0348610. Date of Electronic Publication: 2026 May 11 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Fuzzy Logic* , Normal Distribution* , Classification Algorithms*, Datasets as Topic |
| Περίληψη: | This study presents a novel multi-view TSK fuzzy system that integrates deformable Gaussian membership functions with a rule-level attention mechanism (MDA-TSK-FS), aiming to improve the modeling capacity and flexibility of fuzzy systems in high-dimensional and complex classification tasks. In the antecedent part, learnable deformation offsets are introduced, enabling the membership function centers of each fuzzy rule to dynamically adjust according to data characteristics. This design enhances the adaptability of rules to the input space. Furthermore, a multi-head attention mechanism at the rule level is incorporated to adaptively allocate rule weights based on sample-specific information, thereby enabling dynamic modeling of rule importance and optimized rule selection. Extensive experiments on five public multi-view datasets, including Caltech7, Handwritten, Dermatology, Forest, and EEG, demonstrate that the proposed model consistently achieves superior performance, reaching classification accuracies of 94.38%, 98.62%, 98.58%, 88.57%, and 69.75%, respectively, and outperforming strong baselines. Ablation studies further verify the effectiveness of the two core components: the deformable antecedent structure and the rule-level attention mechanism, which individually improved EEG classification accuracy by approximately 7% and 6%, and jointly by 9.25% compared to the baseline. Notably, the model exhibits superior generalization and interpretability, particularly when processing multi-source heterogeneous data. These findings indicate that the proposed approach provides a new modeling paradigm for multi-view fuzzy inference, offering both theoretical contributions and practical application potential. (Copyright: © 2026 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| Entry Date(s): | Date Created: 20260511 Date Completed: 20260512 Latest Revision: 20260513 |
| Update Code: | 20260513 |
| PubMed Central ID: | PMC13160309 |
| DOI: | 10.1371/journal.pone.0348610 |
| PMID: | 42113778 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!