Conditional Feature Interactions

Conditional Feature Interactions is a modeling framework in machine learning that captures feature dependencies whose effects on the outcome variable are contingent upon the values of other conditioning features, thereby enabling more interpretable and adaptive representations of complex interaction structures.

31

Publications

1.5K

Citations

82

Authors

40

Institutions

Publications per year

2017–2026

6

Authors

82

  1. TO
    Tadashi Ohta

    Soka University, Japan

    3 publications · 51 citations

    3

    h-index

  2. LL
    Luigi Logrippo

    University of Ottawa, Canada

    3 publications · 42 citations

    3

    h-index

  3. EH
    Evan H. Magill

    University of Stirling, United Kingdom

    3 publications · 46 citations

    3

    h-index

  4. XX
    Xun Xu

    University of Auckland, New Zealand

    2 publications · 47 citations

    2

    h-index

  5. MK
    Mario Kolberg

    University of Stirling, United Kingdom

    2 publications · 30 citations

    2

    h-index

1–5 of 82

Venues

  1. FIW

    5 publications · 151 citations

    5

    h-index

  2. Computer Networks

    5 publications · 88 citations

    5

    h-index

  3. Journal of Network and Computer Applications

    JNCA · 1 publications · 10 citations

    1

    h-index

  4. IEEE Transactions on Multimedia

    1 publications · 113 citations

    1

    h-index

  5. Formal Methods in System Design

    1 publications · 42 citations

    1

    h-index

1–5