Misclassification-error-inspired Ensemble of Interpretable First-order TSK Fuzzy Sub-classifiers: A Novel Multi-View Learning Perspective

This study explores a novel interpretable Takagi-Sugeno-Kang (TSK) fuzzy ensemble classifier called MEI-TSK from a multi-view learning perspective. Unlike most existing fuzzy ensemble classifiers where aggregation learning performs only after the training of multiple fuzzy sub-classifiers, MEI-TSK first allows the determination of the antecedents of all fuzzy rules in an individual way for each of TSK fuzzy sub-classifiers. It then employs the proposed misclassification-error-inspired learning to accomplish its ensemble learning by training the consequents of all fuzzy rules of each TSK fuzzy sub-classifier in a multi-view learning way with the simplest averaging aggregation. As a result, the misclassification error caused by such an ensemble learning of fuzzy sub-classifiers is theoretically upper-bounded. MEI-TSK also features the use of both Bernoulli random feature selection and random feature permutation. The permuted features can be conveniently useful for determining all the antecedents of fuzzy rules with diversity guarantee among all the sub-classifiers, and accordingly, be discarded after ensemble learning, resulting in shorter fuzzy rules and improved generalization capability. The experimental results indicate the effectiveness of MEI-TSK in terms of classification performance and/or interpretability.