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 possess an exponential increase in the number of fuzzy rules, and it poses challenges for efficient identification of the parameter matrix in MIMO tasks. A lightweight TSK fuzzy classifier (LW-TSK-FC) is proposed to achieve the balance of training efficiency and predictive performance especially on MIMO tasks. It has the following advantages: (1) An adaptive weighting method based on directly connected FRB enables the efficient generation of fuzzy rules, meanwhile retaining the distribution characteristic of FuCo-FRB to reduce information loss. (2) The consequent network of first-order TSK is optimized to a novel series structure by using matrix factorization. This new series structure increases the depth of consequent network and enable the implementation of kernel function and least learning machine (LLM), enhancing the calculation efficiency and predictive performance. (3) There are only weight parameters in consequent network of LW-TSK-FC and these parameters can be identified quantitatively by LLM, leading to a great training efficiency. The comparison experiments with other 14 power classifiers on 13 public UCI datasets shown comparable predictive performance and outstanding training and testing efficiency in both MISO and MIMO tasks. The experiments on a real-world clinical task demonstrated the significant capability of LW-TSK-FC on handling imbalanced small data, and can provide the semantic interpretability for reasoning process.