证据理论加权决策树在外包维修中的应用

外包维修过程中产生的数据通常是模糊的，传统上很少按重要性分类，从这些数据仅仅能搜索到相关的不确定信息。将证据理论作为处理不确定数据的有力工具，提出用证据理论加权决策树算法对外包维修过程产生的不确定数据进行分类并挖掘出规则。实验结果表明本算法可有效的对外包维修数据进行分类，并可以生成可指导产品研发的规则。

Application Evidence Theory Weighted Decision Tree Algorithm for Outsourced Maintenance

Outsourced maintenance information can affect the product development sustainability, achievement transformation capacity and product quality. Data generated in the processes of outsourced maintenance are inherently vague and seldom classified according to their importance. From these data is only able to search the relevant uncertain information. Since evidence theory is a powerful tool can deal with uncertain data, this paper propose use evidence theory weighted decision tree algorithm to classify uncertain data generated form outsourced maintenance and to dig out the rules. The experimental results shows this algorithm can efficient classify uncertain data and generate rules which can guide the products research and development.