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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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“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].”
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Borbora, Z., Srivastava, J., Hsu, K.-W., & Williams, D. (2011) · 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing · doi:10.1109/passat/socialcom.2011.122 · Cited by 58
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Sobreiro, P., Martinho, D. D. S., Alonso, J. G., & Berrocal, J. (2022) · IEEE Access · doi:10.1109/access.2022.3146397 · Cited by 8
Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach
Culbert, B., Fu, B., Brownlow, J., Chu, C., Meng, Q., & Xu, G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-92013-9_10 · Cited by 2
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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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.
