The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Selective Pre-processing of Imbalanced Data for Improving Classification Performance
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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
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
Cited by
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
Types of minority class examples and their influence on learning classifiers from imbalanced data
Napierala, K., & Stefanowski, J. (2016) · Journal of Intelligent Information Systems · doi:10.1007/s10844-015-0368-1 · Cited by 290
Self-citationScenario-Based Insider Threat Detection From Cyber Activities
Chattopadhyay, P., Wang, L., & Tan, Y.-P. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2857473 · Cited by 115
Metadata
- Authors
- Stefanowski, J., & Wilk, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008-08-29
- Pages
- 283-292
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2171141701
- Cited by
- 188
- References
- 3
- FWCI
- 2.48
- Type
- conference paper
- 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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- CoChat. (2026). Citation check: Selective Pre-processing of Imbalanced Data for Improving Classification Performance. https://app.cochat.ai/papers/10.1007/978-3-540-85836-2_27
- The paper
- Stefanowski, J., & Wilk, S. (2008). Selective Pre-processing of Imbalanced Data for Improving Classification Performance. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-85836-2_27
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.
