Internally and Generatively Decorrelated Ensemble of First-Order Takagi–Sugeno–Kang Fuzzy Regressors With Quintuply Diversity Guarantee
Erhao Zhou, Chi‐Man Vong, Yusuke Nojima, Shitong Wang
Abstract
While the recently developed first-order Takagi–Sugeno–Kang (TSK) fuzzy regressor FIMG-TSK shares its full interpretability, this study leverages the concisely expressed output variance of FIMG-TSK to explore its high feasibility in being a wide-ensemble component. In this way, the regression performance can be enhanced and simultaneously FIMG-TSKs overdependence on the rule weights can be alleviated to a certain extent. To this end, a wide ensemble of all base regressors (i.e., FIMG-TSKs) called EFIMG-TSKs is proposed. In the ensemble-strategic aspect, EFIMG-TSK has its internally and generatively decorrelated ensemble strategy with a quintuply diversity guarantee for its strong generalization capability. In the learning aspect, the learning objective of EFIMG-TSKs reflects the internally and generatively decorrelated ensemble learning of all base FIMG-TSKs and accordingly is optimized globally with an analytical solution to the weights of all fuzzy rules in each base FIMG-TSK. The experimental results on 16 benchmarking datasets demonstrate the effectiveness of EFIMG-TSKs in terms of regression performance, training time, and interpretability.