SETL: a transfer learning based dynamic ensemble classifier for concept drift detection in streaming data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
https://link.springer.com/content/pdf/10.1007/s10586-023-04149-w.pdf
Reference40 articles.
1. Mayaki, M.Z.A., Riveill, M.: Autoregressive based Drift Detection Method. Int. Jt. Conf. Neural Netw. 1, 1 (2022). https://doi.org/10.1109/IJCNN55064.2022.9892066
2. Jain, M., Kaur, G.: Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network data. Clust. Comput. 24, 2099–2114 (2021). https://doi.org/10.1007/s10586-021-03249-9
3. Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD ’01, pp. 377–382. ACM Press, New York (2001)
4. Brzezinski, D., Stefanowski, J.: Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 25, 81–94 (2014). https://doi.org/10.1109/TNNLS.2013.2251352
5. Myint, T.M., Lynn, K.T.: Handling the concept drifts based on ensemble learning with adaptive windows. IAENG Int. J. Comput. Sci. 48, 1–16 (2021)
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