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Customer churn prediction using improved balanced random forests
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Research works printed but not linked: 19
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Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks
López Iturriaga, F. J., & Sanz, I. P. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.11.025 · Cited by 199
CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions
Verhoef, P. C., Venkatesan, R., McAlister, L., Malthouse, E. C., Krafft, M., & Ganesan, S. (2010) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2010.02.009 · Cited by 160
Idris, A., Rizwan, M., & Khan, A. (2012) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2012.09.001 · Cited by 139
A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108
“This is because a balanced dataset composed of churned and retained customers has higher noise tolerance than a imbalanced dataset, hence it is more likely to be able to obtain decision boundaries for minor groups, which in this case denotes the churned customer dataset [91].”
Background
Metadata
- Authors
- Xie, Y., Li, X., Ngai, E. W. T., & Ying, W.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-04
- Volume
- 36
- Issue
- 3
- Pages
- 5445-5449
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2061798849
- Cited by
- 412
- References
- 0
- FWCI
- 26.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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- CoChat. (2026). Citation check: Customer churn prediction using improved balanced random forests. https://app.cochat.ai/papers/10.1016/j.eswa.2008.06.121
- The paper
- Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.06.121
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
