Constructing Interpretable Belief Rule Bases Using a Model-Agnostic Statistical Approach

Belief rule base (BRB) has attracted considerable interest due to its interpretability and exceptional modeling accuracy. Generally, BRB construction relies on prior knowledge or historical data. The limitations of knowledge constrain the knowledge-based BRB and are unsuitable for use in large-scale rule bases. Data-driven techniques excel at extracting model parameters from data, thus significantly improving the accuracy of BRB. However, the previous data-based BRBs neglected the study of interpretability, and some still depend on prior knowledge or introduce additional parameters. All these factors make the BRB highly problem-specific and limit its broad applicability. To address these problems, a model-agnostic statistical BRB (MAS-BRB) modeling approach is proposed in this article. It adopts an MAS methodology for parameter extraction, ensuring that the parameters both fulfill their intended roles within the BRB framework and accurately represent complex, nonlinear data relationships. A comprehensive interpretability analysis of MAS-BRB components further confirms their compliance with established BRB interpretability standards. Experiments conducted on multiple public datasets demonstrate that MAS-BRB not only achieves improved modeling performance but also shows greater effectiveness compared to existing rule-based and traditional machine learning models.