Abstract
Class imbalance presents significant challenges to customer churn prediction. Most of previous research efforts addressing class imbalance focus on the usage of in-domain information. In fact, due to the development of information technology, customer data of related domains may be gathered. These data come from different time-periods, districts or product categories. In this paper, we develop a new churn prediction model based on transfer learning model, which uses customer data from related domains to address the issue of data imbalance. The new model is applied to a real-world churn prediction problem in the bank industry. The results show that the new model provides better performance than traditional method such as resampling and cost-sensitive learning in dealing with class balance.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (Grant No. 71071101 and No. 71172196) and Scientific Research Starting Foundation for Young Teachers of Sichuan University (2012SCU11013).
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Zhu, B., Xiao, J., He, C. (2014). A Balanced Transfer Learning Model for Customer Churn Prediction. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55182-6_9
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DOI: https://doi.org/10.1007/978-3-642-55182-6_9
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