The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Training and assessing classification rules with imbalanced data
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Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
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Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,155
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 20,825
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Density Estimation for Statistics and Data Analysis
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Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,630
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Cited by
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129
Wongvorachan, T., He, S., & Bulut, O. (2023) · Information · doi:10.3390/info14010054 · Cited by 375
Credit Risk Analysis Using Machine and Deep Learning Models
Addo, P., Guegan, D., & Hassani, B. (2018) · Risks · doi:10.3390/risks6020038 · Cited by 317
A broad review on class imbalance learning techniques
Rezvani, S., & Wang, X. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110415 · Cited by 281
Metadata
- Authors
- Menardi, G., & Torelli, N.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2012-10-30
- Volume
- 28
- Issue
- 1
- Pages
- 92-122
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2117190680
- Cited by
- 765
- References
- 73
- FWCI
- 9.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Training and assessing classification rules with imbalanced data. https://app.cochat.ai/papers/10.1007/s10618-012-0295-5
- The paper
- Menardi, G., & Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-012-0295-5
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.
