Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

Advancement and application of rule-based expert systems have been a key research area in explainable artificial intelligence (XAI) because the rule-base is one most common natural frameworks for knowledge representation. The present work aims to design novel system, called Cumulative Belief Rule-Based System (CBRBS), by establishing efficient modeling inference procedures, where procedure includes generation cumulative belief rules via numeric data transformation extended rule integration, consistent activation activated integration. All these procedures enable CBRBS better achieve balance explainability, high-efficiency (or computing complexity), accuracy fit with different scenarios, as well overcome limitations classical systems. Extensive experiments based on well-known pipeline leak detection problem open-source classification problems are conducted illustrate feature advantage over other some commonly used classifiers.