Horizontal progressive and longitudinal leapfrogging fuzzy classification with feature activity adjustment

Classification accuracy and interpretability are crucial importance for recognizing seizures based on electroencephalogram (EEG) signals. This study presents a novel deep ladder-type Takagi–Sugeno–Kang (TSK) fuzzy classifier (D-LT-TSK) that alternately utilizes horizontal progressive learning and longitudinal leapfrogging learning styles. Based on the nonuniform probability distribution co-generated by the distance correlation (DC) coefficient and random bias matrix, a feature activity adjustment mechanism (DC-FAM) is adopted to adjust the activity of each feature to realize the evolution from full connection to partial connection between the input layer and rule layers of the TSK classifier. Feedforward and feedback neural networks are combined to learn consequent parameters in the Then-part of fuzzy rules, for the sake of strengthening the approximation performance and achieving fast converge capability. To take full advantage of valuable decision-making information, D-LT-TSK is learned in the horizontal progressive and longitudinal leapfrogging learning style by mapping the decision-making information of learning modules into the original input space. Experimental results demonstrated that (1) the highly interpretable D-LT-TSK be capable of yielding satisfactory classification performance by utilizing short fuzzy rules, and (2) the optimization algorithm in the Then-part enhanced the approximation performance and accelerate the convergence speed.