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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There is not one oversampling technique that always yields the best result.
“It is clear that there is not one oversampling technique that always yield the best result (Amin et al., 2016).”
From Data engineering for fraud detection (2021) · cites this paper as Amin et al., 2016
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Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
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Synthetic resampling strategies and machine learning for digital soil mapping in Iran
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Enhancing customer retention in telecom industry with machine learning driven churn prediction
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Systematic review of class imbalance problems in manufacturing
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Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets
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A Review of the Oversampling Techniques in Class Imbalance Problem
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A Novel Ensemble Framework for an Intelligent Intrusion Detection System
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Enhanced Skin Lesions Classification Using Deep Convolutional Networks
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Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE)
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Oversampling imbalanced data in the string space
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Fair evaluation of classifier predictive performance based on binary confusion matrix
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A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers
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Research on customer churn prediction and model interpretability analysis
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A Multidepth and Multibranch Network for Hyperspectral Target Detection Based on Band Selection
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Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance
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An efficient feature selection framework based on information theory for high dimensional data
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Just-in-time customer churn prediction in the telecommunication sector
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Case based models of the relationship between consumer resistance to innovation and customer churn
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Antimicrobial Resistance and Machine Learning: Challenges and Opportunities
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Just-in-time Customer Churn Prediction: With and Without Data Transformation
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Optimal Feature Selection for Imbalanced Text Classification
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Self-citationEnsemble-based deep learning techniques for customer churn prediction model
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FCM-CSMOTE: Fuzzy C-Means Center-SMOTE
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Farthest SMOTE: A Modified SMOTE Approach
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Employee Turnover Prediction Using Machine Learning
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Foundations on Imbalanced Classification
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Self-citation
- 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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