Multivariable fuzzy rule-based models and their granular generalization: A visual interpretable framework

Fuzzy rule-based models have been widely used due to their interpretability and effectiveness. However, they still encounter challenges when dealing with multivariable and large-scale data. In this study, we first propose a novel approach to establish a selective sampling and mapping data reduction method. The method focuses on reducing data variables while decreasing the number of samples, and an appropriate scaling size can be chosen for different situations. Then, a multivariable data-driven fuzzy rule-based model is developed based on the processed data. Moreover, the data projection approach using the distance metric helps to preserve the structural characteristics of the original data. The results are visually presented to facilitate an interpretable description of the subsequent rule-based modeling. Furthermore, due to the inevitable inaccuracy in the projection process of numeric modeling, we introduce the allocation of information granularity to extend the model to a granular form at a more abstract level. Experimental studies on both synthetic and publicly available datasets demonstrate that the proposed method has superior effectiveness and efficiency compared to the existing state-of-the-art regression algorithms.(c) 2022 Published by Elsevier B.V.

Automated summary

In this study, we propose a novel approach for selective sampling and mapping data reduction to address the challenges in dealing with multivariable and large-scale data. The approach focuses on reducing data variables and samples while preserving the structural characteristics of the original data. A multivariable data-driven fuzzy rule-based model is then developed based on the processed data. Experimental studies demonstrate that the proposed method outperforms existing regression algorithms in terms of effectiveness and efficiency.