Disposition-Based Concept Drift Detection and Adaptation in Data Stream
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-022-06653-4.pdf
Reference43 articles.
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3. de Barros, R.S.M.; Hidalgo, J.I.G.; de Lima Cabral, D.R.: Wilcoxon rank sum test drift detector. Neurocomputing 275, 1954–1963 (2018)
4. Frías-Blanco, I.; del Campo-Ávila, J.; Ramos-Jimenez, G.; Morales-Bueno, R.; Ortiz-Díaz, A.; Caballero-Mota, Y.: Online and non-parametric drift detection methods based on Hoeffding’s bounds. IEEE Trans. Knowl. Data Eng. 27(3), 810–823 (2014)
5. Shao, J.; Ahmadi, Z.; Kramer, S.: Prototype-based learning on concept-drifting data streams. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 412–421 (2014)
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