The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Learning from streaming data with concept drift and imbalance: an overview
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,842
Probability Inequalities for sums of Bounded Random Variables
Hoeffding, W. (1994) · Springer Series in Statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,666
Probability Inequalities for Sums of Bounded Random Variables
Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500830 · Cited by 4,852
Ensemble based systems in decision making
Polikar, R. (2006) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2006.1688199 · Cited by 2,938
Self-citationImproved boosting algorithms using confidence-rated predictions
Schapire, R. E., & Singer, Y. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279960 · Cited by 2,547
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
Ensemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050
A Survey on Ensemble Learning for Data Stream Classification
Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579
A Systematic Study of Online Class Imbalance Learning With Concept Drift
Wang, S., Minku, L. L., & Yao, X. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2771290 · Cited by 331
Metadata
- Authors
- Hoens, T. R., Polikar, R., & Chawla, N. V.
- Venue
- Progress in Artificial Intelligence
- Published
- 2012-01-13
- Volume
- 1
- Issue
- 1
- Pages
- 89-101
- ISSN
- 2192-6352 (ISSN-L)
- 2192-6360
- OpenAlex
- W2109964623
- Cited by
- 301
- References
- 83
- FWCI
- 7.92
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Hoens, T. R., Polikar, R., & Chawla, N. V. (2012). Learning from streaming data with concept drift and imbalance: an overview. Progress in Artificial Intelligence. https://doi.org/10.1007/s13748-011-0008-0
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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.
