An Augmented Learning Approach for Multiple Data Streams Under Concept Drift

Author:

Wang KunORCID,Lu JieORCID,Liu AnjinORCID,Zhang GuangquanORCID

Publisher

Springer Nature Singapore

Reference28 articles.

1. Álvarez, V., Mazuelas, S., Lozano, J.A.: Minimax classification under concept drift with multidimensional adaptation and performance guarantees. In: International Conference on Machine Learning, pp. 486–499. PMLR (2022)

2. Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: In 4th International Workshop on Knowledge Discovery from Data Streams. Citeseer (2006)

3. Balzanella, A., Verde, R.: Histogram-based clustering of multiple data streams. Knowl. Inf. Syst. 62(1), 203–238 (2020)

4. Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448. SIAM (2007)

5. Chandra, S., Haque, A., Khan, L., Aggarwal, C.: An adaptive framework for multistream classification. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 1181–1190. Indianapolis, IN, USA (2016)

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