Feature Synthesis

Feature Synthesis is a domain-specific feature construction technique that systematically generates new features by combining, transforming, or deriving existing ones through principled operations to enhance representational power in machine learning models.

105

Publications

34.4K

Citations

296

Authors

131

Institutions

Publications per year

2017–2026

49

Authors

296

  1. BB
    Bir Bhanu

    University of California, Riverside, United States

    4 publications · 313 citations

    4

    h-index

  2. DL
    Dan Levi

    General Motors (United States), United States

    3 publications · 100 citations

    3

    h-index

  3. MA
    Mathieu Acher

    Université de Rennes, France

    3 publications · 120 citations

    3

    h-index

  4. DB
    Don Batory

    The University of Texas at Austin, United States

    3 publications · 260 citations

    3

    h-index

  5. SS
    Shai Silberstein

    General Electric (Israel), Israel

    3 publications · 100 citations

    3

    h-index

1–5 of 296

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 233 citations

    7

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 5 publications · 29.4K citations

    5

    h-index

  3. The Journal of Chemical Physics

    2 publications · 123 citations

    2

    h-index

  4. Neural Computing and Applications

    2 publications · 35 citations

    2

    h-index

  5. Expert Systems with Applications

    2 publications · 61 citations

    2

    h-index

1–5