A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)

Author:

Niu ZequnORCID,Guo Wenjie,Xue Jingfeng,Wang YongORCID,Kong Zixiao,Huang Lu

Funder

National Natural Science Foundation of China

Major Scientific and Technological Innovation Project of Shandong Province

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference34 articles.

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3. Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M. A.,. USAD: UnSupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, pp. 3395–3404. doi:10.1145/3394486.3403392.

4. The power of ensembles for active learning in image classification;Beluch,2018

5. Mcdal: maximum classifier discrepancy for active learning;Cho;IEEE Trans Neural Netw Learn Syst,2022

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