Semi-supervised Outlier Detection
Semi-Supervised Outlier Detection is an outlier detection approach that belongs to the class of machine learning methods leveraging labeled data for normal instances and unlabeled data for training, distinguished by its reliance on partial supervision to model normal behavior and identify deviations without requiring labeled anomalies.
91
Publications
6.8K
Citations
313
Authors
138
Institutions
Publications per year
2017–2026
63
Parents
91
1–5 of 91
313
4
h-index
3
h-index
3
h-index
2
h-index
2
h-index
1–5 of 313
138

Northwestern Polytechnical University
Xi'an, China
4 h-index
10 publications
378 citations
Nanjing University of Science and Technology
Nanjing, China
4 h-index
14 publications
312 citations

Beijing University of Posts and Telecommunications
Beijing, China
3 h-index
9 publications
643 citations

University of Colorado System
Boulder, United States
3 h-index
6 publications
170 citations

Indian Institute of Technology Bombay
Mumbai, India
3 h-index
8 publications
140 citations
Tsinghua University
Beijing, China
2 h-index
5 publications
150 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
2 h-index
10 publications
565 citations
Jiangsu University of Science and Technology
Zhenjiang, China
2 h-index
6 publications
69 citations
1–8 of 138
Repository · 7 publications · 334 citations
7
h-index
3 publications · 108 citations
3
h-index
3 publications · 57 citations
3
h-index
2 publications · 103 citations
2
h-index
2 publications · 126 citations
2
h-index
1–5