Regularization Method for Rule Reduction in Belief Rule-based SystemRegularization Method for Rule Reduction in Belief Rule-based System

Abstract

Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.