Feature Set Optimization

Feature Set Optimization is a methodological process in machine learning and data analysis that systematically selects, transforms, or reduces a collection of input features to improve model performance, interpretability, or computational efficiency while preserving essential information relevant to the learning task.

274

Publications

14.2K

Citations

949

Authors

478

Institutions

Publications per year

2017–2026

154

Variants

Feature Optimization

Authors

949

  1. SM
    Seyedali Mirjalili

    Torrens University Australia, Australia

    5 publications · 777 citations

    5

    h-index

  2. MH
    Mohammad H. Nadimi-Shahraki

    Islamic Azad University of Najafabad, Iran

    4 publications · 547 citations

    4

    h-index

  3. HJ
    Heming Jia

    Northeast Forestry University, China

    3 publications · 71 citations

    3

    h-index

  4. MB
    Marcel Brun

    Texas A&M University, United States

    3 publications · 47 citations

    3

    h-index

  5. ER
    Edward R. Dougherty

    Texas A&M University, United States

    3 publications · 47 citations

    3

    h-index

1–5 of 949

Venues

  1. IEEE Access

    13 publications · 651 citations

    13

    h-index

  2. Expert Systems with Applications

    6 publications · 683 citations

    6

    h-index

  3. Neural Computing and Applications

    6 publications · 584 citations

    6

    h-index

  4. PLoS ONE

    4 publications · 230 citations

    4

    h-index

  5. Scientific Reports

    4 publications · 164 citations

    4

    h-index

1–5