Decision Tree Pruning

Decision Tree Pruning is a model simplification technique that belongs to the class of regularization methods in machine learning, specifically distinguished by its removal of branches or subtrees from a decision tree to reduce overfitting by improving generalization performance on unseen categorical data.

333

Publications

25.1K

Citations

845

Authors

416

Institutions

Publications per year

2017–2026

85

Authors

845

  1. DJ
    David Jensen

    University of Massachusetts Amherst, United States

    4 publications · 223 citations

    4

    h-index

  2. OT
    Olcay Taner Yıldız

    Işık University, Türkiye

    3 publications · 50 citations

    3

    h-index

  3. TO
    Tim Oates

    University of Maryland, Baltimore County, United States

    3 publications · 205 citations

    3

    h-index

  4. IA
    Isabelle Alvarez

    Laboratoire d'Ingénierie des Systèmes Complexes, France

    3 publications · 35 citations

    3

    h-index

  5. CS
    Clayton Scott

    University of Michigan, United States

    3 publications · 49 citations

    3

    h-index

1–5 of 845

Venues

  1. Expert Systems with Applications

    8 publications · 517 citations

    8

    h-index

  2. Information Sciences

    5 publications · 326 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 108 citations

    5

    h-index

  4. Journal of Artificial Intelligence Research

    JAIR · 4 publications · 3.4K citations

    4

    h-index

  5. Intelligent Data Analysis

    IDA · 4 publications · 181 citations

    4

    h-index

1–5