The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Cost-sensitive boosting for classification of imbalanced data
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
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
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
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309
Survey on deep learning with class imbalance
Johnson, J. M., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0192-5 · Cited by 3,032
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
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
Metadata
- Authors
- Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2007-12
- Volume
- 40
- Issue
- 12
- Pages
- 3358-3378
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2103614420
- Cited by
- 1,423
- References
- 12
- FWCI
- 29.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Cost-sensitive boosting for classification of imbalanced data. https://app.cochat.ai/papers/10.1016/j.patcog.2007.04.009
- The paper
- Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007). Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition. https://doi.org/10.1016/j.patcog.2007.04.009
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.
