Negative Correlation Learning for Customer Churn Prediction: A Comparison Study
Joint Authors
Rodan, Ali
Fayyoumi, Ayham
Faris, Hossam
Alsakran, Jamal
Al-Kadi, Omar
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-23
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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 achievebetter generalization performance (high churn rate) compared with ensembleof MLP without NCL (flat ensemble) and other common datamining techniques used for churn analysis.
American Psychological Association (APA)
Rodan, Ali& Fayyoumi, Ayham& Faris, Hossam& Alsakran, Jamal& Al-Kadi, Omar. 2015. Negative Correlation Learning for Customer Churn Prediction: A Comparison Study. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1078790
Modern Language Association (MLA)
Rodan, Ali…[et al.]. Negative Correlation Learning for Customer Churn Prediction: A Comparison Study. The Scientific World Journal No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1078790
American Medical Association (AMA)
Rodan, Ali& Fayyoumi, Ayham& Faris, Hossam& Alsakran, Jamal& Al-Kadi, Omar. Negative Correlation Learning for Customer Churn Prediction: A Comparison Study. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1078790
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078790