Zang, Zhen ShengYin, RuiLu, WeiPedrycz, WitoldZhang, Li-Yong2025-04-182025-04-182024Zang, 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.10636706http://dx.doi.org/10.1109/TFUZZ.2024.3394897https://hdl.handle.net/20.500.12713/6575Classification 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.eninfo:eu-repo/semantics/closedAccessDeep Fuzzy Classification SystemFeature Transformation and ReconstructionLinguistic İnterpretabilityTakagi-Sugeno-Kang (TSK)ModelArticle32842974311WOS:0012911578000142-s2.0-85192176238Q110.1109/TFUZZ.2024.3394897Q1