Rule Extraction

Rule Extraction is a machine learning interpretability method that derives symbolic rules from complex, opaque predictive models to approximate and explain their decision logic in human-understandable terms.

1.2K

Publications

73K

Citations

2.8K

Authors

1.1K

Institutions

Publications per year

2017–2026

242

Authors

2.8K

  1. YH
    Yoichi Hayashi

    Meiji University, Japan

    27 publications · 1.6K citations

    19

    h-index

  2. RS
    Rudy Setiono

    National University of Singapore, Singapore

    18 publications · 1.9K citations

    16

    h-index

  3. GB
    Guido Bologna

    University of Geneva, Switzerland

    13 publications · 404 citations

    13

    h-index

  4. CP
    Chee Peng Lim

    Deakin University, Australia

    11 publications · 716 citations

    11

    h-index

  5. HI
    Hisao Ishibuchi

    Osaka Prefecture University, Japan

    11 publications · 489 citations

    10

    h-index

1–5 of 2.8K

Venues

  1. IEEE Transactions on Fuzzy Systems

    TFS · 34 publications · 3.9K citations

    28

    h-index

  2. Expert Systems with Applications

    33 publications · 1.5K citations

    25

    h-index

  3. IEEE Transactions on Neural Networks

    20 publications · 3K citations

    20

    h-index

  4. Knowledge-Based Systems

    18 publications · 1.8K citations

    16

    h-index

  5. 16

    h-index

1–5