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Adversarial knowledge augmentation based learning for enhancing interpretable fuzzy-rule-based classification
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DOI：10.1016/j.knosys.2026.116775.png)
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Abstract
En 中文
• Adversarial knowledge augmentation (KA2) motivated by advanced cognitive research. • An interpretable fuzzy classifier TSK2A2 proposed to boost accuracy by KA2. • A novel interpretable AND-NOT operator-based augmented fuzzy rule is provided. • A fast KA2-based adversarial training method is developed to enhance TSK2A2. • TSK2A2 owns a superior trade-off among accuracy, interpretability, and efficiency.
Keywords:
Adversarial training
Interpretable fuzzy rules
Knowledge augmentation
TSK fuzzy classifiers
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7.6
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4.5W
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