Benchmarking sampling techniques for imbalance learning in churn prediction
系统比较了多种先进采样方法在客户流失预测中的表现，使用最大利润准则评估，发现采样效果依赖于评价指标和分类器，并针对不同情况推荐了合适的采样策略。
Class imbalance presents significant challenges to customer churn prediction. Many data-level sampling solutions have been developed to deal with this issue. In this paper, we comprehensively compare the performance of several state-of-the-art sampling techniques in the context of churn prediction. A recently developed maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost–benefit. The experimental results show that the impact of sampling methods depends on the used evaluation metric and that the impact pattern is interrelated with the classifiers. An in-depth exploration of the reaction patterns is conducted, and suitable sampling strategies are recommended for each situation. Furthermore, we also discuss the setting of the sampling rate in the empirical comparison. Our findings will offer a useful guideline for the use of sampling methods in the context of churn prediction.