Incremental Decision Rules

Incremental Decision Rules is a computational decision-making framework that iteratively refines and updates rule-based models by incorporating new evidence or data over time, distinguishing itself from static or batch decision rules through its adaptive, stepwise revision process.

26

Publications

822

Citations

54

Authors

36

Institutions

Publications per year

2017–2026

5

Authors

54

  1. TT
    Tzu-Liang Tseng

    The University of Texas at El Paso, United States

    3 publications · 127 citations

    3

    h-index

  2. TL
    Tianrui Li

    Southwest Jiaotong University, China

    3 publications · 128 citations

    3

    h-index

  3. FJ
    Francisco J. Ferrer-Troyano

    Universidad de Sevilla, Spain

    2 publications · 69 citations

    2

    h-index

  4. JC
    José C. Riquelme

    Universidad de Sevilla, Spain

    2 publications · 69 citations

    2

    h-index

  5. YF
    Yu-Neng Fan

    National Taiwan University, Taiwan

    2 publications · 116 citations

    2

    h-index

1–5 of 54

Venues

  1. Electronic Notes in Theoretical Computer Science

    ENTCS · 2 publications · 71 citations

    2

    h-index

  2. 1

    h-index

  3. Applied Economics

    1 publications · 11 citations

    1

    h-index

  4. Computational Linguistics

    1 publications · 31 citations

    1

    h-index

  5. 1

    h-index

1–5