Cost-complexity Pruning

Cost-Complexity Pruning is a model simplification technique in decision tree learning that belongs to the class of post-pruning methods, distinguished by its use of a complexity parameter to balance the trade-off between tree size and classification accuracy through nested subtree selection.

13

Publications

387

Citations

37

Authors

20

Institutions

Publications per year

2017–2026

4

Authors

37

  1. HP
    Hua Peng

    Sun Yat-sen University, China

    1 publications · 33 citations

    1

    h-index

  2. JA
    Jon A. Steingrimsson

    Brown University, United States

    1 publications · 59 citations

    1

    h-index

  3. KT
    Kwok‐Leung Tsui

    City University of Hong Kong, Hong Kong SAR China

    1 publications · 21 citations

    1

    h-index

  4. HB
    Heitor Blesa Farias

    Universidade Federal de Minas Gerais, Brazil

    1 publications · 14 citations

    1

    h-index

  5. MA
    Massimo Aria

    University of Naples Federico II, Italy

    1 publications · 17 citations

    1

    h-index

1–5 of 37

Venues

  1. INFORMS journal on computing

    2 publications · 58 citations

    2

    h-index

  2. Energies

    1 publications · 33 citations

    1

    h-index

  3. Review of Computer Engineering Research

    RCER · 1 publications · 25 citations

    1

    h-index

  4. Knowledge and Information Systems

    1 publications · 72 citations

    1

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 1 publications · 22 citations

    1

    h-index

1–5