A Cognitively “AND”-Biased Adversarial TSK Fuzzy Classifier: Training and Generalization Analysis

Abstract

This study attempts to develop a novel cognitive-biased knowledge adversarial learning method for an interpretable Takagi–Sugeno–Kang (TSK) fuzzy classifier to enhance its generalization capability. Based on the cognitive bias that humans occasionally tend to understand “and” in a rule as a union rather than an intersection for the rule’s interpretability, the concept of the cognitively “and”-biased adversarial TSK fuzzy classifier based on the cognitively “and”-biased adversarial attacks is proposed, and accordingly, the cognitively “and”-biased adversarial training method called CAT for the proposed classifier is derived to earn enhanced generalization capability. This study has three distinct merits: 1) the cognitively “and”-biased adversarial attacks on the fuzzy operators in fuzzy rules are invented as a novel means of adversarial attacks to mimic the human cognitive bias appearing in the rule’s interpretability; 2) after training the proposed TSK fuzzy classifier with CAT, the resultant fuzzy rules reflect the cognitively biased behaviors in the sense of the transformation level from “and” to “or” operations for the first time by the randomly generated uninorm rather than frequently used fuzzy “and” operators; and 3) CAT has its theoretical guarantee of enhanced generalization and overfitting-avoidance capabilities. Extensive experimental results demonstrate that CAT has satisfactory classification performance and good interpretability.