Churn Prediction Based on Fusion of Deep Learning and Ensemble Learning

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    As the China’s communication market has been saturated over time, the competition among telecom operators is becoming increasingly fierce. Churn prediction of customers has turned into one of the most concerns for telecom operators. This study proposes a method based on multi-model fusion to create a churn prediction model of customers. First, through bootstrap sampling and positive-negative sample balancing, multiple training datasets are obtained from the original training data. Then, base models are trained by these datasets with ensemble learning and deep learning algorithms. Finally, the base models are merged into a high-level model. The experimental results prove that the fusion model performs better than all base models in the test datasets, with a practical value for production.

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  • Received:October 09,2020
  • Revised:November 16,2020
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  • Online: June 05,2021
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