IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, VOL. 1, NO. 6, DECEMBER 2017 421 Stacked-Structure-Based Hierarchical Takagi-Sugeno-Kang Fuzzy Classification Through Feature Augmentation Ta Zhou, Hisao Ishibuchi , Fellow, IEEE, and Shitong Wang Abstract—In this paper, a new stacked-structure-based hi- erarchical Takagi–Sugeno–Kang (TSK) fuzzy classifier called SHFA-TSK-FC with both promising performance and high in- terpretability is proposed to tackle with the shortcoming of the existing hierarchical fuzzy classifiers in interpreting the outputs and fuzzy rules of intermediate layers. In order to achieve the en- hanced classification performance, each component unit, which is a zero-order TSK fuzzy classifier, in SHFA-TSK-FC is organized in a stacked way such that all the input features of the original training samples plus the interpretable augmented features, corresponding to the interpretable output of each previous component unit, are fed as the
