The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
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
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
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
Learning from imbalanced data: open challenges and future directions
Krawczyk, B. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-016-0094-0 · Cited by 2,459
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
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
Effective data generation for imbalanced learning using conditional generative adversarial networks
Douzas, G., & Bacao, F. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.030 · Cited by 601
Boosting methods for multi-class imbalanced data classification: an experimental review
Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., & Asadpour, M. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00349-y · Cited by 452
Metadata
- Authors
- Fernández, A., López, V., Galar, M., del Jesus, M. J., & Herrera, F.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 42
- Pages
- 97-110
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2164341120
- Cited by
- 388
- References
- 5
- FWCI
- 22.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. https://app.cochat.ai/papers/10.1016/j.knosys.2013.01.018
- The paper
- Fernández, A., López, V., Galar, M., del Jesus, M. J., & Herrera, F. (2013). Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2013.01.018
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.
