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Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study
Open accessAbstract
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.
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“It is clear that there is not one oversampling technique that always yield the best result (Amin et al., 2016).”
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Metadata
- Authors
- Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A.
- Affiliations
- Institute of Management Sciences Peshawar (PK)
- University of Oxford (GB)
- Information Technology University (PK)
- Taibah University (SA)
- University of Stirling (GB)
- Venue
- IEEE Access
- Published
- 2016
- Volume
- 4
- Pages
- 7940-7957
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2540642777
- Cited by
- 310
- References
- 81
- Citations by year
- 2026: 17 · 2025: 37 · 2024: 38 · 2023: 31 · 2022: 45 · 2021: 55 · 2020: 31 · 2019: 28 · 2018: 19 · 2017: 9
- FWCI
- 15.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Stirling Online Research Repository (University of Stirling) (submittedVersion, public-domain)
- Research Output (Edinburgh Napier University) (submittedVersion, cc-by)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion, other-oa)
- Funders
- Engineering and Physical Sciences Research Council (EP/M026981/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- class imbalance, oversampling, undersampling, customer churn prediction, customer retention, telecom customers, synthetic minority oversampling, ADASYN, reverse k nearest neighbors, rule generation, genetic algorithm, LEM2, covering algorithm, classification performance comparison
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