Cost-sensitive Active Learning

Cost-Sensitive Active Learning is an active learning paradigm that, as a refinement of query selection strategies, incorporates the varying costs associated with acquiring different types of labels or querying different instances, thereby distinguishing itself from standard active learning by optimizing not only for model performance but also for cost efficiency.

53

Publications

2.5K

Citations

151

Authors

79

Institutions

Publications per year

2017–2026

26

Authors

151

  1. FM
    Fan Min

    Southwest Petroleum University, China

    4 publications · 114 citations

    4

    h-index

  2. FP
    Foster Provost

    New York University, United States

    4 publications · 249 citations

    4

    h-index

  3. PZ
    Peilin Zhao

    Tencent (China), China

    3 publications · 202 citations

    3

    h-index

  4. AA
    Alekh Agarwal

    Microsoft (United States), United States

    3 publications · 70 citations

    3

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  5. QW
    Qingyao Wu

    South China University of Technology, China

    2 publications · 74 citations

    2

    h-index

1–5 of 151

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 206 citations

    7

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  2. Neurocomputing

    3 publications · 170 citations

    3

    h-index

  3. 2

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  4. Knowledge-Based Systems

    2 publications · 78 citations

    2

    h-index

  5. IEEE Geoscience and Remote Sensing Letters

    GRSL · 1 publications · 13 citations

    1

    h-index

1–5