A Novel Modeling Approach for Cumulative Belief Rule-Base With Joint Optimization and Rule Synthesis

Cumulative belief rule-based system (CBRBS) is a recent representative of explainable artificial intelligence (XAI). However, the use of CBRBS as XAI still faces many challenges, e.g., over-reliance on expert experience and applying unreasonable rule synthesis in the existing modeling process. Hence, a novel modeling approach is proposed for constructing CBRBS in the aim of providing a better XAI, in which a joint optimization model is proposed first to describe the mathematical model of parameter and structure optimization, and the corresponding algorithm is further designed to automatically achieve the joint optimization of CBRBS. Afterward, a domain-based calculation method of synthesis factor is proposed to develop a new rule synthesis method for CBRBS, which not only achieves the reduction of inefficient and inconsistent rules but also takes into account interpretability and generalization ability. In experimental analysis, the proposed modeling approach is employed to construct CBRBS for handling rice taste assessment and benchmark classification problems. The comparison results show that the proposed approach makes it possible for CBRBS to achieve a good balance between model complexity and inference accuracy. More importantly, the resulting CBRBS has better accuracy and lower complexity than some existing rule-based systems and classical classifiers.