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

Authors:
E Xu;Liangshan Shao;Xuedong Gao;Baofeng Zhai
Affiliations:
Liaoning Institute of Technology, China;Liaoning Technical University, China;University of Science and Technology Beijing, China;Liaoning Institute of Technology, China
Venue:
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
Year:
2006

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Abstract

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 churn. 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.