Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Customer Churn Prediction for Broadband Internet Services
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
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The customer churn problem affects hugely the telecommunication services in particular, and businesses in general. Note that in majority of cases the number of potential customer churn is much smaller than the non-churners. Therefore, the imbalance distribution of samples between churners and non-churners is a concern when building a churn prediction model. This paper presents a Local PCA approach to solve imbalance classification problem by generating new churn samples. The experiments were carried out on a large real-world Telecommunication dataset and assessed on a churn prediction task. The experiments showed that the Local PCA along with Smote outperformed Linear regression and Standard PCA data generation techniques.