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Cited by 310Imbalanced Data Classification TechniquesCustomer churn and segmentationMachine Learning and Data Classification

The DOI matches its Crossref record; 78 of the 81 references it cites were found in a registry or an index.

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

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Amin, Adnan, Anwar, Sajid, Adnan, Awais, Nawaz, Muhammad, Howard, Newton, Qadir, Junaid, Hawalah, Ahmad, Hussain, Amir·2016·IEEE Access
DOI 10.1109/access.2016.2619719
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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.

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Show all 81 references

Research works printed but not linked: 11

  1. 2015, KDD’06 Challenge Dataset
  2. 2015, Data source
  3. 2015, Data source
  4. 2014, Standardize Function
  5. 2016, IBM Telecom Dataset
  6. dhurjad, 2014, A survey on oversampling techniques for imbalanced learning, International Journal of Engineering Innovation and Management, 3, 279
  7. nguyen, 2003, Analysis of STULONG data by rough set exploration system (RSES), Proc ECML/PKDD Workshop, 71
  8. chitra, 2011, Customer retention in banking sector using predictive data mining technique, Proc 5th Int Conf Inf Technol, 1
  9. satyasree, 2013, An exhaustive literature review on class imbalance problem, Int J Emerg Trends Sci Technol, 2, 109
  10. japkowicz, 2000, The class imbalance problem: Significance and strategies, Proc Int Conf Artif Intell, 1
  11. 2015, Choosing Between a Nonparametric Test and a Parametric Test

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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)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016
Volume
4
Pages
7940-7957
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2016.2619719
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
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
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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