A new rule reduction and training method for extended belief rule base based on DBSCAN algorithm

Rule reduction is one of the focuses of numerous researches on belief-rule-based system, in some cases, too many redundant rules may be a concern to the rule-based system. Though rule reduction methods have been widely used in the belief-rule-based system, extended belief-rule-based system, which is an expansion of belief-rule-based system, still lacks methods to reduce and train rules in the extended belief rule base (EBRB). To this end, this paper proposes an EBRB reduction and training method. Based on the density-based spatial clustering applications with noise (DBSCAN) algorithm, a new EBRB reduction method is proposed, where all the rules in the EBRB will be visited and rules within the distance of the fusion threshold will be fused. Moreover, the EBRB training method using parameter learning, which uses a set of training data to train the parameters of EBRB, is also proposed to improve the accuracy of the EBRB system. Two case studies of regression and classification are used to illustrate the feasibility and efficiency of the proposed EBRB reduction and training method. Comparison results show that the proposed method can effectively downsize the EBRB and increase the accuracy of EBRB system.