Probabilistic Decision Trees

Probabilistic Decision Trees is a machine learning model that extends traditional decision trees by incorporating probabilistic transitions at nodes, enabling the representation of uncertainty in both predictions and structure through probability distributions over class labels or outcomes.

79

Publications

5.3K

Citations

223

Authors

124

Institutions

Publications per year

2017–2026

26

Variants

Stochastic Decision Trees

Authors

223

  1. JE
    Jonathan E. Fieldsend

    University of Exeter, United Kingdom

    3 publications · 90 citations

    3

    h-index

  2. VS
    Vitaly Schetinin

    University of Bedfordshire, United Kingdom

    3 publications · 90 citations

    3

    h-index

  3. UM
    Udi Manber

    University of Arizona, United States

    3 publications · 69 citations

    3

    h-index

  4. RE
    Richard Everson

    University of Exeter, United Kingdom

    3 publications · 90 citations

    3

    h-index

  5. WJ
    W. J. Krzanowski

    University of Exeter, United Kingdom

    3 publications · 90 citations

    3

    h-index

1–5 of 223

Venues

  1. Artificial Intelligence in Medicine

    AIM · 2 publications · 274 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 64 citations

    2

    h-index

  3. International Journal of Computer Vision

    1 publications · 62 citations

    1

    h-index

  4. Ecological Informatics

    1 publications · 22 citations

    1

    h-index

  5. ACM Transactions on Embedded Computing Systems

    TECS · 1 publications · 19 citations

    1

    h-index

1–5