A Mixed Process Neural Network and its Application to Churn Prediction in Mobile Communications

Authors:
Guojie Song;Dongqing Yang;Ling Wu;Tengjiao Wang;Shiwei Tang
Affiliations:
Peking University, Beijing, P.R. China;Peking University, Beijing, P.R. China;Peking University, Beijing, P.R. China;Peking University, Beijing, P.R. China;Peking University, Beijing, P.R. China
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

Churn prediction is an increasingly pressing issue in today's ever-competitive commercial environments, especially in mobile communication arena. In this paper, a Mixed Process Neural Network (MPNN) based on fourier orthogonal base function has been proposed to support churn management, which can deal with both static value and time-varied continuous value simultaneously. To further improve its performance, an optimized network, c- MPNN, has been presented, which adopts fourier expansion based preprocessing and hidden layer combination techniques to optimize MPNN's structure. Most important of all, our method has been used in real applications in China Mobile. Experiments based on the real datasets also show that our proposed churn prediction method has good maneuverability and performance.