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Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study

Amin, Adnan; Anwar, Sajid; Adnan, Awais; Nawaz, Muhammad; Howard, Newton; Qadir, Junaid; Hawalah, Ahmad; Hussain, Amir

Authors

Adnan Amin

Sajid Anwar

Awais Adnan

Muhammad Nawaz

Newton Howard

Junaid Qadir

Ahmad Hawalah



Abstract

Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring the future behavior of the customers. However, the performances of predictive models are greatly affected when the real-world data set is highly imbalanced. A data set is called imbalanced if the samples size from one class is very much smaller or larger than the other classes. The most commonly used technique is over/under sampling for handling the class-imbalance problem (CIP) in various domains. In this paper, we survey six well-known sampling techniques and compare the performances of these key techniques, i.e., mega-trend diffusion function (MTDF), synthetic minority oversampling technique, adaptive synthetic sampling approach, couples top-N reverse k-nearest neighbor, majority weighted minority oversampling technique, and immune centroids oversampling technique. Moreover, this paper also reveals the evaluation of four rules-generation algorithms (the learning from example module, version 2 (LEM2), covering, exhaustive, and genetic algorithms) using publicly available data sets. The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.

Citation

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016). Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study. IEEE Access, 4, 7940-7957. https://doi.org/10.1109/ACCESS.2016.2619719

Journal Article Type Article
Acceptance Date Oct 1, 2016
Online Publication Date Oct 26, 2016
Publication Date 2016
Deposit Date Oct 7, 2019
Publicly Available Date Oct 7, 2019
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 4
Pages 7940-7957
DOI https://doi.org/10.1109/ACCESS.2016.2619719
Keywords SMOTE, ADASYN, mega trend diffusion function, class imbalance, rough set, customer churn, mRMR. ICOTE, MWMOTE, TRkNN
Public URL http://researchrepository.napier.ac.uk/Output/1792667

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