Construction and Reasoning Approach of Belief Rule-Base for Classification Base on Decision Tree

The classical belief rule-based (BRB) systems are usually constructed by arranging and combining referential values of antecedent attributes or by setting special fixed values, which can lead to overly large size of BRB systems in complex problems. This paper combines the decision tree classification method to analyze the information of data and extract the rules. Based on this, a new rule representation method with referential interval is proposed and the rule base is constructed according to the support degree and belief degree of the data. In the newly proposed method, the introduction of decision tree ensures that the size of the rule base is reasonable. Moreover, the rule parameters trained by the differential evolution (DE) algorithm are optimized and adjusted to further improve the system performance. The experiments are conducted on several commonly used public classification datasets. And the proposed algorithm can achieve better accuracy results compared with classical classification methods and the existing classification methods of BRB systems on average. The experimental results validate the reasonableness and effectiveness of the BRB construction method proposed in this paper.