Entropy-based Splitting

Entropy-Based Splitting is a decision tree partitioning method that belongs to the class of impurity-based splitting criteria, distinguished by its use of Shannon entropy to quantify node heterogeneity and guide recursive data segmentation.

16

Publications

587

Citations

44

Authors

26

Institutions

Publications per year

2017–2026

6

Authors

44

  1. RD
    Rocco De Rosa

    University of Milan, Italy

    2 publications · 34 citations

    2

    h-index

  2. NC
    Nicolò Cesa‐Bianchi

    University of Milan, Italy

    2 publications · 34 citations

    2

    h-index

  3. MA
    Miguel Angel Medina‐Pérez

    Tecnológico de Monterrey, Mexico

    1 publications · 15 citations

    1

    h-index

  4. UD
    Uwe D. Hanebeck

    Karlsruhe Institute of Technology, Germany

    1 publications · 242 citations

    1

    h-index

  5. HC
    H. C. Yee

    Ames Research Center, United States

    1 publications · 19 citations

    1

    h-index

1–5 of 44

Venues

  1. 1

    h-index

  2. Knowledge-Based Systems

    1 publications · 15 citations

    1

    h-index

  3. Econstor (Econstor)

    Repository · 1 publications · 10 citations

    1

    h-index

  4. COMBINATORICA

    1 publications · 95 citations

    1

    h-index

  5. Knowledge and Information Systems

    1 publications · 15 citations

    1

    h-index

1–5