Human-in-the-loop Classification

Human-in-the-Loop Classification is a classification paradigm within machine learning and data science that integrates human expertise as an active component in the iterative training, refinement, and decision-making processes of automated classification systems.

33

Publications

1.7K

Citations

170

Authors

86

Institutions

Publications per year

2017–2026

28

Authors

170

  1. ED
    Eduard Dragut

    Temple University, United States

    2 publications · 62 citations

    2

    h-index

  2. LH
    Lihong He

    Temple University, United States

    2 publications · 62 citations

    2

    h-index

  3. AH
    Andreas Holzinger

    Medical University of Graz, Austria

    2 publications · 942 citations

    2

    h-index

  4. SV
    Slobodan Vučetić

    Temple University, United States

    2 publications · 62 citations

    2

    h-index

  5. TH
    Tao Hu

    Wuhan University, China

    1 publications · 23 citations

    1

    h-index

1–5 of 170

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 34 citations

    2

    h-index

  2. IEEE Access

    1 publications · 19 citations

    1

    h-index

  3. Journal of Neuroscience Methods

    1 publications · 18 citations

    1

    h-index

  4. Journal of Medical Internet Research

    1 publications · 23 citations

    1

    h-index

  5. ACM Transactions on Interactive Intelligent Systems

    TIIS · 1 publications · 36 citations

    1

    h-index

1–5