A novel extended rule-based system based on K-Nearest Neighbor graph

• Published in: Information sciences Vol. 662; p. 120158
• Main Authors: Fu, Yang-Geng, Lin, Xin-Yi, Fang, Geng-Chao, Li, Jin, Cai, Hong-Yi, Gong, Xiao-Ting, Wang, Ying-Ming
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2024
• Subjects: Extended belief rule-based system; Graph convolution neural network; Hierarchical navigable small world graph; K-Nearest Neighbor graph; Extended belief rule-based system; Hierarchical navigable small world graph; Graph convolution neural network; K-Nearest Neighbor graph
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120158

The Belief Rule-Based (BRB) system faces the rule combination explosion issue, making it challenging to construct the rule base efficiently. The Extended Belief Rule-Based (EBRB) system offers a solution to this problem by using data-driven methods. However, using EBRB system requires the traversal of the entire rule base, which can be time-consuming and result in the activation of many irrelevant rules, leading to an incorrect decision. Existing search optimization methods can somewhat solve this issue, but they have limitations. Moreover, the calculation of the rule activation weight only considers the similarity between input data and a single rule, ignoring the influence of the rule linkage. To address these problems, we propose a new EBRB system based on the K-Nearest Neighbor graph index (Graph-EBRB). We introduce the Hierarchical Navigable Small World (HNSW) algorithm to create the K-Nearest Neighbor graph index of the EBRB system. This index allows us to efficiently search and activate a set of key rules. We also propose a new activation weight calculation method based on the Graph Convolution Neural Network (GCN), and we optimize the system performance using a parameter learning strategy. We conduct a comprehensive experiment on 14 commonly used public data sets, and the results show that Graph-EBRB system significantly improves the reasoning efficiency and accuracy of the EBRB system. Finally, we apply the Graph-EBRB system to tree disease identification and achieve excellent classification performance, identifying over 90% of the diseased trees on the complete dataset.