Customer churn prediction in class imbalance dataset
基于不平衡数据集的客户流失预测研究*

Class imbalance is one of main obstacles in customer churn prediction. To overcome this problem, this paper applied undersampling technology to reducing class imbalance, and applied 5 classifiers, such as C4.5D, C4.5N, RIPPER, NaiveBayes and RandomForest, to predicting customer churn. Undersampling technology increased predictive accuracy of customer churn by sacrificing predictive accuracy of normal customer, according to experimental results. So, applied resample technology to preprocessing customer churn dataset. Proved resample technology to make up shortage of undersampling techno-logy. So, resample technology is more suitable for customer churn dataset, which have characteristic of class imbalance.