A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication

This paper explores the possible application of a single SVM classifier and its variants to churner identification problem in mobile telecommunication industry in which the role of customer retention program becomes more important than ever due to its very competitive business environment. In particular, this study introduces a uniformly sub sampled ensemble model of SVM classifiers combined with principal component analysis (PCA) not only to reduce the high dimensionality of data but also to boost the reliability and accuracy of calibrated models on data sets with highly skewed class distributions. According to our experiments, the performance of USE SVM model with PCA is superior to all compared models and the number of principal components (PCs) affect the accuracy of ensemble models.