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The Scientific World Journal
Volume 2015, Article ID 473283, 7 pages
Research Article

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

1King Abdulla II School for Information Technology, The University of Jordan, Amman 11942, Jordan
2College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia

Received 17 June 2014; Revised 23 August 2014; Accepted 7 September 2014

Academic Editor: Shifei Ding

Copyright © 2015 Ali Rodan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.