Stacked-Structure-Based Hierarchical Takagi-Sugeno-Kang Fuzzy Classification Through Feature Augmentation

In this paper, a new stacked-structure-based hierarchical Takagi-Sugeno-Kang (TSK) fuzzy classifier called SHFA-TSK-FC with both promising performance and high interpretability 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 enhanced 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 input features of the current component unit. These augmented features can essentially open the manifold structure of the original input space such that the enhanced classification performance can be expected. In designing each component unit, its analytical solution to the consequent parts of fuzzy rules therein is obtained quickly by using the least learning machine such that SHFA-TSK-FC becomes scalable for large datasets. Its high interpretability is guaranteed by randomly selecting the input features and randomly choosing the fixed five Gaussian membership functions for the selected input features in the premise of each fuzzy rule. Experimental results on real-life datasets and an application case demonstrate the enhanced or at least comparable classification performance and high interpretability of SHFA-TSK-FC.