Random clustering forest for extended belief rule-based system

The extended belief rule-based (EBRB) system is an enhancement of the traditional belief rule-based (BRB) system, which extends the belief distribution of the antecedent attributes. Compared with BRB system, EBRB system has stronger knowledge expression ability. EBRB system uses relational data to generate rules based on traditional belief rule base, which is simple and effective. However, EBRB system needs to traverse all the rules in the rule base and has the problems of inefficiency and inconsistency. Although the existing search optimization methods can solve this problem to some extent, they generally have the shortcomings of insufficient generalization ability. In view of this, this paper proposes a new rule search optimization algorithm based on K-means clustering tree (KMT-EBRB). By combining with the bagging algorithm, an EBRB system based on random clustering forest (RKMF-EBRB) is implemented. To evaluate the performance of the developed model, this paper chooses 12 common UCI datasets for two groups of experiments. Firstly, the properties of EBRB system based on K-means tree and K-means forest are studied, and then the new method is compared with existing methods. The experimental results show that K-means tree can improve the search efficiency of EBRB system, while random K-means clustering forest can further improve the accuracy and stability of EBRB reasoning.

Automated summary

The EBRB system is an enhancement of the traditional BRB system with stronger knowledge expression ability, but it faces issues of inefficiency and inconsistency. This paper proposes a new rule search optimization algorithm based on K-means clustering tree and implements an EBRB system based on random clustering forest, which improves system performance according to experimental results.