Knowledge Inference of Automotive After-Sales Service Based on Rough Set and D_S Theory
Wang Hu
Wang Rui
Zeng Zhu
Abstract：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 calculat-ed 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 .
Keywords：customer behaviorrough setD_S evidential theory
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 35-40 )