基于粗糙集与D_S证据理论的汽车售后服务知识推理

Knowledge Inference of Automotive After-Sales Service Based on Rough Set and D_S Theory

通过汽车用户的行为特征对用户的车辆进行售后服务知识推理对汽车售后服务质量的提升有着重要的意义。利用粗糙集与信息熵理论从众多的用户行为属性中提取出对汽车部件的状态有显著影响的客户特征行为属性作为推理证据,运用决策规则强度对各证据对应的基本概率赋值(BPA)进行了确定,在此基础上运用D_S证据理论对各证据进行了合成,推理出客户的服务需求。通过实例证明了该方法可用于汽车售后服务知识推理。

Using the data of customer′s behavior characteristics to predict the customer′s demand for serv-ice has significance to improve the quality of automotive after-sales service .This paper rough set and entro-py theory are made use of to extract the characteristic attributes from a large number of customer behavior attributes , which have significant effect on the state of the automotive parts .The reasoning evidences are constituted by these characteristic attributes .The BPA（ basic probability assignment ） corresponding to ev-ery evidence is calculated by decision rules′intensity .Meanwhile , the comprehensive evidence is calculated by using the D_S evidential theory to synthesize the BPA .The customers′service requirements can be inferred by this method .The method is proved by a case that it can be used for car′s after-sales service knowledge reasoning .