The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Learning from class-imbalanced data: Review of methods and applications
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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
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
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
Logistic Regression in Rare Events Data
King, G., & Zeng, L. (2001) · Political Analysis · doi:10.1093/oxfordjournals.pan.a004868 · Cited by 4,316
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
Hand, D. J., & Till, R. J. (2001) · Machine Learning · doi:10.1023/a:1010920819831 · Cited by 2,276
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
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
Building Markov state models with solvent dynamics
Gu, C., Chang, H.-W., Maibaum, L., Pande, V. S., Carlsson, G. E., & Guibas, L. J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s2-s8 · Cited by 21
Cited by
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088
“In the previous decade the number of publications on imbalance learning problem started growing rapidly [11, 16].”
BackgroundMethodResultInfluentialDeep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
Artificial intelligence, bias and clinical safety
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Metadata
- Authors
- Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 73
- Pages
- 220-239
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2562319768
- Cited by
- 2,377
- References
- 9
- FWCI
- 80.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Learning from class-imbalanced data: Review of methods and applications. https://app.cochat.ai/papers/10.1016/j.eswa.2016.12.035
- The paper
- Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2016.12.035
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.
