Abstract
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.
Highlights
With the deepening of informatization in various fields, a large amount of raw data has emerged
N represents the number of referential values of consequent attribute. θk and δik denote the weight of the kth rule and the weight of the ith antecedent attribute in the kth rule, respectively
This paper proposes a new method for constructing belief rule-based (BRB) system with referential interval for classification on decision trees
Summary
The BRB system proposed by Yang et al [7] contains two aspects: the BRB representation and the BRB inference methodology using the evidential reasoning approach. The section briefly introduces the relevant theoretical knowledge of BRB system
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