Extended Belief Rule Base Inference Model Based on KD Tree

The extended belief rule base (EBRB) system has been widely used in decision-making problems for its accuracy and efficiency. However, EBRB system needs to traverse all the rules in the rule base and has the problems of inefficiency and inconsistency. In view of this, an extended belief rule base system inference method based on a k-dimensional (KD) tree is implemented in this paper. First, the KD tree is introduced in the construction of rule base. Then, the K-Nearest Neighbor (KNN) query optimization algorithm, based on the space indexing technique of the KD tree, is used to search for key rules. Next, the obtained key rules are activated to participate in the inference process. In addition, several experiments are conducted on function fitting, oil pipeline leakage simulation and classification datasets from UCI to verify the inference performance of the proposed method. The experimental results illustrate that the extended belief rule base system based on the KD tree can effectively improve the accuracy and stability of EBRB reasoning. © (2024), (International Association of Engineers). All rights reserved.