Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10594
Title: Customer churn prediction using improved balanced random forests
Authors: Xie, Y
Li, X
Ngai, EWT 
Ying, W
Keywords: Churn prediction
Imbalanced data
Random forests
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
Source: Expert systems with applications, 2009, v. 36, no. 3 part 1, p. 5445-5449 How to cite?
Journal: Expert Systems with Applications 
Abstract: Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests.
URI: http://hdl.handle.net/10397/10594
DOI: 10.1016/j.eswa.2008.06.121
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