The DOI matches its OpenAlex record; 25 of the 25 references it cites were found in a registry or an index.
The effect of class distribution on classifier learning: an empirical study
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
SMOTE synthetic minority over-sampling technique
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
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
(1992) · Cited by 23,431
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
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Bauer, E., & Kohavi, R. (1999) · Machine Learning · doi:10.1023/a:1007515423169 · Cited by 2,638
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.
Kubát, M., & Matwin, S. (1997) · Cited by 2,230
The foundations of cost-sensitive learning
Elkan, C. (2001) · Cited by 1,945
Cited by
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
Barua, S., Islam, M. M., Yao, X., & Murase, K. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.232 · Cited by 1,150
Learning when training data are costly: the effect of class distribution on tree induction
Weiss, G. M., & Provost, F. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1199 · Cited by 921
Self-citationTraining and assessing classification rules with imbalanced data
Menardi, G., & Torelli, N. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0295-5 · Cited by 765
Metadata
- Authors
- Weiss, G. M., & Provost, F. J.
- Published
- 2001-01-01
- OpenAlex
- W1574715085
- Cited by
- 352
- References
- 25
- FWCI
- 12.77
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The effect of class distribution on classifier learning: an empirical study. https://app.cochat.ai/papers/10.7282/t3-vpfw-sf95
- The paper
- Weiss, G. M., & Provost, F. J. (2001). The effect of class distribution on classifier learning: an empirical study. https://doi.org/10.7282/t3-vpfw-sf95
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.
