SABeDM: a sliding adaptive beta distribution model for concept drift detection in a dynamic environment
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-023-02004-3.pdf
Reference37 articles.
1. Guo H, Li H, Sun N, Ren Q, Zhang A, Wang W (2023) Concept drift detection and accelerated convergence of online learning. Knowl Inf Syst 65(3):1005–1043. https://doi.org/10.1007/s10115-022-01790-6
2. Widmer G (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–101. https://doi.org/10.1007/bf00116900
3. Abbasi A, Javed AR, Chakraborty C, Nebhen J, Zehra W, Jalil Z (2021) ElStream: an ensemble learning approach for concept drift detection in dynamic social big data stream learning. IEEE Access 9:66408–66419. https://doi.org/10.1109/ACCESS.2021.3076264
4. Shahraki A, Abbasi M, Taherkordi A, Jurcut AD (2022) A comparative study on online machine learning techniques for network traffic streams analysis. Comput Netw 207:108836. https://doi.org/10.1016/j.comnet.2022.108836
5. Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2013) A survey on concept drift adaptation. ACM Comput Surv 1(1):35. https://doi.org/10.1145/0000000.0000000
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