Abstract: This paper proposes a new classification approach based on improved belief rule-base reasoning by introducing linear combinational mode, setting the number of rules based on the classifications and improving the method of calculating individual matching degree. Compared with the traditional belief rule-base inference methodology, the number of rules in the proposed method does not depend on the number of antecedent attributes or its referential values, and it is only related to classification number. In this way, the new method can ensure the applicability for complex problems. In the experiments, the differential evolution algorithm is applied to train parameters, including rule weights, attribute weights, referential values of antecedent attributes and belief degrees. Three commonly public datasets have been employed to validate the proposed method. And the classification results are proved to be ideal, which shows that the proposed method is reasonable and effective.

Key words: belief rule-base, belief rule-base inference methodology using evidence reasoning (RIMER）, parameter learning, classification method

摘要： 通过引入置信规则库的线性组合方式，设定规则数等于分类数及改进个体匹配度的计算方法，提出了基于置信规则库推理的分类方法。比较传统的置信规则库推理方法，新方法中规则数的设置不依赖于问题的前件属性数量或候选值数量，仅与问题的分类数有关，保证了方法对于复杂问题的适用性。实验中，通过差分进化算法对置信规则库的规则权重、前件属性权重、属性候选值和评价等级的置信度进行参数学习，得到最优的参数组合。对3个常用的公共分类数据集进行测试，均获得理想的分类准确率，表明新分类方法合理有效。

关键词: 置信规则库, 基于证据推理的置信规则库推理方法（RIMER）, 参数学习, 分类方法