Polynomial Feature Selection

Polynomial Feature Selection is a feature selection method that identifies a subset of relevant polynomial combinations of original variables to improve model performance by reducing dimensionality while preserving nonlinear relationships in the data.

44

Publications

3.2K

Citations

146

Authors

67

Institutions

Publications per year

2017–2026

20

Authors

146

  1. JL
    Julio L. Peixoto

    University of Houston, United States

    2 publications · 156 citations

    2

    h-index

  2. NH
    Nick Haber

    Stanford University, United States

    1 publications · 40 citations

    1

    h-index

  3. AC
    Aaron Cohen

    Oregon Health & Science University, United States

    1 publications · 38 citations

    1

    h-index

  4. HE
    Hakan Ezgi Kızılöz

    University of Turkish Aeronautical Association, Türkiye

    1 publications · 337 citations

    1

    h-index

  5. YZ
    Yang Zhao

    Beijing Institute of Technology, China

    1 publications · 144 citations

    1

    h-index

1–5 of 146

Venues

  1. The American Statistician

    2 publications · 156 citations

    2

    h-index

  2. Applied Intelligence

    2 publications · 547 citations

    2

    h-index

  3. Neurocomputing

    2 publications · 378 citations

    2

    h-index

  4. Knowledge and Information Systems

    2 publications · 459 citations

    2

    h-index

  5. 2

    h-index

1–5