Handling Class Imbalance in Mobile Telecoms Customer Churn Prediction
Class imbalance is a major problem that is often experienced when dealing with rare events, such as churn recognition in the mobile telecommunications industry. In this paper, various strategies of addressing the problem are studied and a demonstration of how under-sampling and Synthetic Minority Oversampling TEchnique (SMOTE) can be used to address the problem is given. The two techniques are implemented individually first, and then the authors take the hybrid approach by combining both SMOTE and under-sampling. For performance evaluation, two predictive techniques, C4.5 decision tree and Naive Bayes classifier with 10-fold cross validation are used.