关键词: 混合TSK模糊分类器, 癫痫脑电图（EEG）信号检测, 对抗训练方法, 基于特征重要性的可解释性, 语义可解释性

Abstract: In recent years, deep TSK (Takagi-Sugeno-Kang) fuzzy classifiers based on stacked ensemble structures have become a key focus in the research of TSK fuzzy classifiers. Compared with traditional single TSK fuzzy classifiers, deep TSK fuzzy classifiers not only exhibit enhanced generalization capability but also offer better interpretability. However, when applying deep TSK fuzzy classifiers to epileptic electroencephalogram（EEG）signal detection, the following two challenges should be addressed: (1) How to improve the existing deep structures to ensure accurate detection of epileptic EEG signals while accelerating model construction and enhancing model interpretability (e.g., the less fuzzy rules and providing two types of interpretabilities); (2) How to leverage human cognitive behavior to further enhance the generalization capability of deep TSK fuzzy classifiers. To address the above two challenges, an adversarial hybrid TSK fuzzy classifier (AH-TSK) for epileptic EEG signal detection is proposed, which primarily consists of two aspects: For the challenge (1), based on the existing deep stacked ensemble structures, by introducing the wide ensemble structure, a novel deep-and-wide-based hybrid ensemble structure is proposed to integrate a linear sub-model called sparse representation-based linear classifier (i.e., SRLc) and several nonlinear sub-models called adversarial TSK fuzzy sub-model (i.e., A-TSK); For the challenge (2), inspired by two cognitive behaviors “from global coarse to local refinement” and “knowledge abandonment”, a new adversarial training method is proposed. It first trains a linear model (SRLc) on all original samples of EEG dataset to classify nonlinear distributed samples. Then, an adversarial training method by introducing “knowledge abandonment” is proposed to train several A-TSKs parallelly on the obtained nonlinear distributed samples of the EEG dataset. Finally, the final output of AH-TSK can be obtained by adopting the proposed nearest-labelled strategy on the outputs of an SRLc and all A-TSKs. Experimental results on the EEG datasets confirm that compared with the comparative methods, AH-TSK exhibits enhanced generalization capability, faster running speed, and better interpretability. Moreover, AH-TSK provides more types of interpretabilities, i.e., linguistic and attribute-significance-based interpretability.

Key words: hybrid Takagi-Sugeno-Kang (TSK) fuzzy classifier, epileptic electroencephalogram (EEG) signals detection, adversarial training method, attribute-significance-based interpretability, linguistic interpretability