A Lightweight TSK Fuzzy Classifier With Quantitative Equivalent Fuzzy Rules via Adaptive Weighting

The first-order Takagi–Sugeno–Kang (TSK) fuzzy classifier with a fully combined fuzzy rule base (FuCo-FRB) is a potent and interpretable classifier for multiple input and multiple output (MIMO) tasks. However, FuCo-FRB poses an exponential increase in the number of fuzzy rules, which creates challenges for efficient identification of the parameter matrix for MIMO tasks. To address these challenges, we propose a lightweight TSK fuzzy classifier (LW-TSK-FC) to achieve the balance of training efficiency and predictive performance, particularly for MIMO tasks. The proposed LW-TSK-FC has three key advantages: 1) an adaptive weighting method based on directly connected FRB enables the efficient generation of fuzzy rules while retaining the distribution characteristics of FuCo-FRB; 2) the consequent network of the first-order TSK is optimized to a novel series structure by using matrix factorization. This new structure increases the depth of the consequent network, enabling the implementation of kernel functions and least learning machine (LLM), which enhance both calculation efficiency and predictive performance; and 3) the consequent network of LW-TSK-FC contains only weight parameters, which can be quantitatively identified using LLM. This results in significant improvements in training efficiency. Comparison experiments demonstrate that LW-TSK-FC achieved superior predictive performance and training efficiency, particularly for tasks involving higher-order statistical features. Furthermore, experiments on a real-world clinical application show that LW-TSK-FC efficiently handled 1000-dimensional data and achieved the best testing AUC. This lightweight structure holds tremendous potential for application in deep or stacked fuzzy neural networks.