An Algorithm for Predicting Customer Churn via BP Neural Network Based on Rough Set

摘要：

To solve the prediction of customer churn, the paper proposed a new algorithm. Based on rough set theory, the algorithm used the consistency of condition attributes and decision attributes in information table, and the conception of super-cube and scan vector to discretize the continuous attributes, reduce the redundant attributes. And furthermore, it took BP neural network as the calculating tool to predict customer chum. The experimental results showed the refined data by rough set was more concise and more convenient to be applied in BP neural network, whose prediction result was more accurate. So, the algorithm via BP neural network based on rough set theory is efficient and effective.

作者: E Xu Shao Liangshan Gao Xuedong Zhai Baofeng

作者单位: Department of Computer Science, Liaoning Institute of Technology, Jinzhou 121001, China Management School, Liaoning Technical University, Fuxin 123000, China Management School, University of Science and Technology Beijing, Beijing 100083, China Software School, Liaoning Institute of Technology, Jinzhou 121001, China

会议类型: 国际会议

会议名称: 2006 Asia-Pacific Services Computing Conference(IEEE亚太地区服务计算会议)

会议地点: 广州

会议语种:英文

页码: 47-50

在线出版日期: 2006-12-12（万方平台首次上网日期，不代表论文的发表时间）