Publisher
Springer Nature Switzerland
Reference20 articles.
1. Ghomeshi, H., Gaber, M.M., Kovalchuk, Y.: EACD: evolutionary adaptation to concept drifts in data streams. Data Min. Knowl. Disc. 33(3), 663–694 (2019)
2. Harel, M., Mannor, S., El-Yaniv, R., Crammer, K.: Concept drift detection through resampling. In: International Conference on Machine Learning, pp. 1009–1017 (2014)
3. Gomes, H.M., Barddal, J.P., Enembreck, F., Bifet, A.: A survey on ensemble learning for data stream classification. ACM Comput. Surv. 50(2), 1–36 (2017). https://doi.org/10.1145/3054925
4. Hewahi, N.M., Kohail, S.N.: Learning concept drift using adaptive training set formation strategy. Int. J. Technol. Diffus. (IJTD) 4(1), 33–55 (2013)
5. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: On demand classification of data streams. In: KDD-2004 – Proc. Tenth ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 503–508 (2004). doi: https://doi.org/10.1145/1014052.1014110