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

Authors

313

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    4 publications · 170 citations

    4

    h-index

  2. SP
    Suyash P. Awate

    University of Utah, United States

    3 publications · 53 citations

    3

    h-index

  3. EA
    Ehsan Adeli

    Stanford University, United States

    3 publications · 154 citations

    3

    h-index

  4. JL
    Jiongqian Liang

    The Ohio State University, United States

    2 publications · 45 citations

    2

    h-index

  5. FS
    Feng Shi

    University of North Carolina at Chapel Hill, United States

    2 publications · 113 citations

    2

    h-index

1–5 of 313

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 334 citations

    7

    h-index

  2. IEEE Access

    3 publications · 108 citations

    3

    h-index

  3. Applied Soft Computing

    3 publications · 57 citations

    3

    h-index

  4. Computers & Security

    2 publications · 103 citations

    2

    h-index

  5. IEEE Sensors Journal

    2 publications · 126 citations

    2

    h-index

1–5