The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Learning with Drift Detection
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Citations
Cites
R: A Language for Data Analysis and Graphics
Ihaka, R., & Gentleman, R. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474713 · Cited by 11,220
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Detection of Abrupt Changes: Theory and Application
Riedel, K. S. (1994) · Technometrics · doi:10.1080/00401706.1994.10485821 · Cited by 3,518
Learning in the Presence of Concept Drift and Hidden Contexts
Widmer, G., & Kubat, M. (1996) · Machine Learning · doi:10.1023/a:1018046501280 · Cited by 1,857
Detection of Abrupt Changes: Theory and Applications.
Peach, N., Basseville, M., & Nikiforov, I. V. (1995) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983416 · Cited by 1,031
Learning drifting concepts: Example selection vs. example weighting
Klinkenberg, R. (2004) · Intelligent Data Analysis · doi:10.3233/ida-2004-8305 · Cited by 438
Detecting Concept Drift with Support Vector Machines
Klinkenberg, R., & Joachims, T. (2000) · Cited by 422
Selecting Examples for Partial Memory Learning
Maloof, M. A., & Michalski, R. S. (2000) · Machine Learning · doi:10.1023/a:1007661119649 · Cited by 142
Adaptive Information Filtering: Learning in the Presence of Concept Drifts
Klinkenberg, R., & Renz, I. (1998) · Cited by 137
Enhancing text classification to improve information filtering
Lanquillon, C. (2001) · Digitalen Hochschulbibliothek Sachsen-Anhalt (Universitäts- und Landesbibliothek Sachsen-Anhalt) · doi:10.25673/4635 · Cited by 23
Cited by
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Self-citationData-driven Soft Sensors in the process industry
Kadlec, P., Gabrys, B., & Strandt, S. (2009) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2008.12.012 · Cited by 1,845
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
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
Self-citationLearning under Concept Drift: A Review
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876857 · Cited by 1,603
Self-citation
Metadata
- Authors
- Gama, J., Medas, P., Castillo, G., & Rodrigues, P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 286-295
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1585854823
- Cited by
- 1,536
- References
- 10
- FWCI
- 5.42
- 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.
Statistics and arithmetic are recomputed, not judged.
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How to cite
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- This page
- CoChat. (2026). Citation check: Learning with Drift Detection. https://app.cochat.ai/papers/10.1007/978-3-540-28645-5_29
- The paper
- Gama, J., Medas, P., Castillo, G., & Rodrigues, P. (2004). Learning with Drift Detection. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-28645-5_29
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.
