Supervised Anomaly Detection
Supervised Anomaly Detection is a supervised learning framework in which a model is trained on labeled data containing both normal and anomalous instances to distinguish between the two classes based on explicit class labels.
185
Publications
9.3K
Citations
671
Authors
303
Institutions
Publications per year
2017–2026
155
Parents
185
Toward Supervised Anomaly Detection
Nico Goernitz, Michael Kloft, Konrad Rieck et al. · Journal of Artificial Intelligence Research · 2013 · 374 citations · Full text
ADBench: Anomaly Detection Benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang et al. · SSRN Electronic Journal · 2022 · 252 citations · Full text
Dapeng Liu, Youjian Zhao, Haowen Xu et al. · 2015 · 250 citations
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303
ETH Zurich
Zurich, Switzerland
6 h-index
15 publications
1.3K citations

The University of Adelaide
Adelaide, Australia
5 h-index
9 publications
785 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
11 publications
820 citations
Oregon State University
Corvallis, United States
3 h-index
12 publications
852 citations
Tencent (China)
Shenzhen, China
3 h-index
7 publications
409 citations

University of Central Florida
Orlando, United States
3 h-index
3 publications
192 citations

University of Padua
Padua, Italy
3 h-index
5 publications
165 citations
University of California, Berkeley
Berkeley, United States
3 h-index
4 publications
233 citations
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Repository · 13 publications · 221 citations
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4 publications · 132 citations
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3 publications · 159 citations
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3 publications · 109 citations
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