A Linguistically Interpretable Deep Fuzzy Classification System With Feature Transformation and Reconstruction
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Classification 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.
Açıklama
Anahtar Kelimeler
Deep Fuzzy Classification System, Feature Transformation and Reconstruction, Linguistic İnterpretability, Takagi-Sugeno-Kang (TSK)Model
Kaynak
IEEE Transactions on Fuzzy Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
32
Sayı
8
Künye
Zang, 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.
