The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Knowledge discovery from imbalanced and noisy data
Overview
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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
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.606 · Cited by 612
Cited by
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
Survey of review spam detection using machine learning techniques
Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0029-9 · Cited by 502
Self-citationOn the effectiveness of preprocessing methods when dealing with different levels of class imbalance
García, V., Sánchez, J. S., & Mollineda, R. A. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2011.06.013 · Cited by 356
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data
Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. (2011) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2010.2084081 · Cited by 300
Self-citationTypes 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
Metadata
- Authors
- Van Hulse, J., & Khoshgoftaar, T.
- Venue
- Data & Knowledge Engineering
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 68
- Issue
- 12
- Pages
- 1513-1542
- ISSN
- 0169-023X (ISSN-L)
- OpenAlex
- W1992795012
- Cited by
- 202
- References
- 4
- FWCI
- 12.65
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Knowledge discovery from imbalanced and noisy data. https://app.cochat.ai/papers/10.1016/j.datak.2009.08.005
- The paper
- Van Hulse, J., & Khoshgoftaar, T. (2009). Knowledge discovery from imbalanced and noisy data. Data & Knowledge Engineering. https://doi.org/10.1016/j.datak.2009.08.005
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.
