融合深度学习与集成学习的用户离网预测

Churn Prediction Based on Fusion of Deep Learning and Ensemble Learning

随着国内通信市场逐渐饱和,电信运营商之间的竞争日趋激烈.用户流失预测已成为电信运营商最关注的问题之一.本文提出一种基于多模型融合的方法创建用户离网预测模型.首先,将原始训练数据经过有放回采样和正负样本平衡得到多份不同的训练数据;然后,利用多份不同的训练数据使用集成学习与深度学习算法训练得到多个基础模型;最终,将多个基础模型进行融合形成高层模型.实验结果表明,融合模型在各类用户测试集上的表现均优于基础模型,具有实际生产应用价值.

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.