A Linguistically Interpretable Deep Fuzzy Classification System With Feature Transformation and Reconstruction

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorZang, Zhen Sheng
dc.contributor.authorYin, Rui
dc.contributor.authorLu, Wei
dc.contributor.authorPedrycz, Witold
dc.contributor.authorZhang, Li-Yong
dc.date.accessioned2025-04-18T08:29:25Z
dc.date.available2025-04-18T08:29:25Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractClassification tasks involving tabular data often require a balance between exceptional performance and heightened interpretability. To address this challenge, we propose a linguistically interpretable deep fuzzy classification system called FFT-FFR-RBFC. The system employs a fuzzy feature transformation (FFT) unit, formed by employing a stacked architecture of multiple Takagi-Sugeno-Kang fuzzy models with nonlinear conclusions, to distill high-level fuzzy features from the input data, a rule-based fuzzy classifier unit to perform classification using these features, while a fuzzy feature reconstruction unit in tandem with the FFT to enhance the system's linguistic interpretability by remapping the high-level features back to their original space. The proposed approach is optimized by minimizing a composite loss function that balances classification and reconstruction losses, ensuring a harmonious interplay between performance and interpretability. Comprehensive evaluation across 20 diverse datasets demonstrates that the system's is exceptionally promising, particularly for high-dimensional or large-scale tabular data classification tasks, achieving superior classification performance while maintaining a high degree of interpretability. © 1993-2012 IEEE.
dc.identifier.citationZang, Z. S., Yin, R., Lu, W., Pedrycz, W., & Zhang, L. Y. (2024). A Linguistically Interpretable Deep Fuzzy Classification System with Feature Transformation and Reconstruction. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3394897
dc.identifier.endpage4311
dc.identifier.issn10636706
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85192176238
dc.identifier.scopusqualityQ1
dc.identifier.startpage4297
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3394897
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6575
dc.identifier.volume32
dc.identifier.wosWOS:001291157800014
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Fuzzy Classification System
dc.subjectFeature Transformation and Reconstruction
dc.subjectLinguistic İnterpretability
dc.subjectTakagi-Sugeno-Kang (TSK)Model
dc.titleA Linguistically Interpretable Deep Fuzzy Classification System With Feature Transformation and Reconstruction
dc.typeArticle

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