2 Matching Annotations
- Feb 2025
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arxiv.org arxiv.org
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H. Ma, B. Ghojogh, M. N. Samad, D. Zheng and M. Crowley, "Isolation Mondrian Forest for Batch and Online Anomaly Detection," 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 2020, pp. 3051-3058, doi: 10.1109/SMC42975.2020.9283073.
The algorithm fuses two ideas, "isolation" from ensemble trees methods for anomaly detection and "Mondrian forests" which can learn flexible regression models from streaming data.
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- Jun 2020
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arxiv.org arxiv.org
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Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2020). Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs. ArXiv:2005.07427 [Cs, Stat]. http://arxiv.org/abs/2005.07427
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