Automatic Feature Construction

Automatic Feature Construction is a methodological approach in machine learning that systematically generates higher-order polynomial features from existing variables through automated transformation, thereby expanding the feature space without manual intervention.

217

Publications

13.9K

Citations

746

Authors

343

Institutions

Publications per year

2017–2026

114

Variants

Automated Feature Construction

Authors

746

  1. LR
    Larry Rendell

    University of Illinois Urbana-Champaign, United States

    5 publications · 391 citations

    5

    h-index

  2. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    5 publications · 288 citations

    5

    h-index

  3. MZ
    Mengjie Zhang

    Victoria University of Wellington, New Zealand

    4 publications · 360 citations

    4

    h-index

  4. TX
    Tao Xiang

    Queen Mary University of London, United Kingdom

    3 publications · 407 citations

    3

    h-index

  5. WN
    Will N. Browne

    Victoria University of Wellington, New Zealand

    2 publications · 23 citations

    2

    h-index

1–5 of 746

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 175 citations

    7

    h-index

  2. IEEE Access

    7 publications · 309 citations

    7

    h-index

  3. 4

    h-index

  4. Sensors

    3 publications · 214 citations

    3

    h-index

  5. Expert Systems with Applications

    3 publications · 87 citations

    3

    h-index

1–5