关键词: 神经网络, 机器学习, 朴素随机过采样, 投票分类

Abstract: The user churn forecast can help the company to reduce the customer loss, has the important significance to the company’s revenue and enhances the competition. However, due to the sparsity and imbalance of telecom field data, most of the users’ loss prediction at home and abroad is in the research stage, and it has not been applied to actual production. In this paper, a hybrid model based on neural network, machine learning and Naive randomoversampler and voting is proposed to predict the lost users in telecommunication field. The dataset uses the KDD Cup 2009 competition data, which is provided by the French telecom operator Orange company. Under 10 cross-validation, AdaBoost and Gradient Boosting, the AUC value can reach 0.677 1 after one vote classification, and other models are used to classify the lost user list of the mixed model to two times, the prediction accuracy of the first 200 high-risk loss users can reach 31.8%. The experimental results show that the accuracy of the model is improved by the combination of Naive randomoversampler and voting.

Key words: neural network, machine learning, Naive randomoversampler, vote classification