The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Automatically countering imbalance and its empirical relationship to cost
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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
Self-citationBreiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Cited by
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
Douzas, G., Bacao, F., & Last, F. (2018) · Information Sciences · doi:10.1016/j.ins.2018.06.056 · Cited by 1,277
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
Multiclass Imbalance Problems: Analysis and Potential Solutions
Wang, S., & Yao, X. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2012.2187280 · Cited by 567
On the use of MapReduce for imbalanced big data using Random Forest
del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.043 · Cited by 328
ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification
Li, M., Xiong, A., Wang, L., Deng, S., & Ye, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105818 · Cited by 66
Metadata
- Authors
- Chawla, N. V., Cieslak, D. A., Hall, L. O., & Joshi, A.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2008-02-17
- Volume
- 17
- Issue
- 2
- Pages
- 225-252
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2123458540
- Cited by
- 255
- References
- 3
- FWCI
- 20.67
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Automatically countering imbalance and its empirical relationship to cost. https://app.cochat.ai/papers/10.1007/s10618-008-0087-0
- The paper
- Chawla, N. V., Cieslak, D. A., Hall, L. O., & Joshi, A. (2008). Automatically countering imbalance and its empirical relationship to cost. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-008-0087-0
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.
