Decision Tree Visualization

Decision Tree Visualization is a graphical representation method that encodes the hierarchical structure and decision logic of a decision tree model through node-link diagrams, where nodes represent decision rules or outcomes and edges denote the branching conditions, thereby enabling intuitive interpretation of classification or regression pathways in machine learning.

51

Publications

3.5K

Citations

173

Authors

110

Institutions

Publications per year

2017–2026

20

Authors

173

  1. YL
    Yan Liu

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 51 citations

    2

    h-index

  2. GS
    Gavriel Salvendy

    Purdue University West Lafayette, United States

    2 publications · 51 citations

    2

    h-index

  3. TB
    Tu Bao Ho

    Japan Advanced Institute of Science and Technology, Japan

    2 publications · 42 citations

    2

    h-index

  4. ST
    Soon Tee Teoh

    University of California, Davis, United States

    2 publications · 112 citations

    2

    h-index

  5. HN
    Hansjörg Neth

    Max Planck Institute for Human Development, Germany

    2 publications · 167 citations

    2

    h-index

1–5 of 173

Venues

  1. Medical Decision Making

    MDM · 3 publications · 49 citations

    3

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 137 citations

    2

    h-index

  3. Bioinformatics

    2 publications · 53 citations

    2

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 381 citations

    1

    h-index

  5. 1

    h-index

1–5