Adversarial Knowledge Augmentation Based Learning for Enhancing Interpretable Fuzzy-rule-based Classification

Interpretable fuzzy-rule-based classification is frequently in great demand for many application scenarios. However, its generalization ability may be hindered by factors such as excessive fuzzy rule parameters and distribution shift. This study aims to improve an interpretable first-order Takagi-Sugeno-Kang (TSK) fuzzy classifier by developing a novel methodology based on adversarial task augmentation, which has recently been an efficient means for enhancing the generalization ability of a classifier. The proposed methodology is designed on the basis of both the conclusive claims in cognitive research and the unique perspective that a fuzzy rule, as a piece of knowledge, bridges input and output data spaces. Accordingly, an innovative concept of adversarial knowledge augmentation (KA2) is proposed, and then the KA2-based interpretable first-order TSK fuzzy classifier (TSK2A2) is developed. TSK2A2 has the following features: (1) Without any direct use of data-related tasks, it augments a virtual data distribution with a small number of interpretable augmented fuzzy rules by means of KA2; (2) Throughout the training of TSK2A2, to have adversarial generation of each augmented fuzzy rule, an AND-NOT operator is introduced to randomly negate certain conditions for its IF-part generation. All the particularly-designed parameters based on the generated IF-parts are applied to the corresponding THEN-parts; (3) TSK2A2 employs fast analytical training for all THEN-parts. Theoretical analysis and/or experimental results on the adopted datasets indicate that TSK2A2 achieves enhanced generalization performance while retaining desirable interpretability and training efficiency.