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A comparative assessment of the performance of ensemble learning in customer churn prediction
Joint Authors
Abbasimehr, Husayn
Setak, Mustafa
Tarokh, Muhammad Jafar
Source
The International Arab Journal of Information Technology
Issue
Vol. 11, Issue 6 (30 Nov. 2014)8 p.
Publisher
Publication Date
2014-11-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Topics
Abstract EN
Customer churn is a main concern of most firms in all industries.
The aim of customer churn prediction is detecting customers with high tendency to leave a company.
Although many modeling techniques have been used in the field of churn prediction, performance of ensemble methods has not been thoroughly investigated yet.
Therefore, in this paper, we perform a comparative assessment of the performance of four popular ensemble methods, i.e., Bagging, Boosting, Stacking, and Voting based on four known base learners, i.e., C4.5 Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Reduced Incremental Pruning to produce Error Reduction (RIPPER).
Furthermore, we have investigated the effectiveness of two different sampling techniques, i.e., oversampling as a representative of basic sampling techniques and Synthetic Minority Over-sampling Technique (SMOTE) as a representative of advanced sampling techniques.
Experimental results show that SMOTE doesn’t increase predictive performance.
In addition, the results show that the application of ensemble learning has brought a significant improvement for individual base learners in terms of three performance indicators i.e., AUC, sensitivity, and specificity.
Particularly, in our experiments, Boosting resulted in the best result among all other methods.
Among the four ensemble methods Boosting RIPPER and Boosting C4.5 are the two best methods.
These results indicate that ensemble methods can be a best candidate for churn prediction tasks.
American Psychological Association (APA)
Abbasimehr, Husayn& Setak, Mustafa& Tarokh, Muhammad Jafar. 2014. A comparative assessment of the performance of ensemble learning in customer churn prediction. The International Arab Journal of Information Technology،Vol. 11, no. 6.
https://search.emarefa.net/detail/BIM-380129
Modern Language Association (MLA)
Abbasimehr, Husayn…[et al.]. A comparative assessment of the performance of ensemble learning in customer churn prediction. The International Arab Journal of Information Technology Vol. 11, no. 6 (Nov. 2014).
https://search.emarefa.net/detail/BIM-380129
American Medical Association (AMA)
Abbasimehr, Husayn& Setak, Mustafa& Tarokh, Muhammad Jafar. A comparative assessment of the performance of ensemble learning in customer churn prediction. The International Arab Journal of Information Technology. 2014. Vol. 11, no. 6.
https://search.emarefa.net/detail/BIM-380129
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-380129