Robust Feature Selection

Robust Feature Selection is a feature selection methodology that systematically identifies and retains informative features under varying data conditions, model assumptions, or perturbations, thereby distinguishing itself from conventional feature selection techniques by its resilience to instability and sensitivity in dynamic or noisy environments.

523

Publications

31.8K

Citations

1.8K

Authors

737

Institutions

Publications per year

2017–2026

275

Authors

1.8K

  1. TL
    Tianrui Li

    Southwest Jiaotong University, China

    14 publications · 656 citations

    13

    h-index

  2. HC
    Hongmei Chen

    Southwest Jiaotong University, China

    14 publications · 615 citations

    13

    h-index

  3. CL
    Chuan Luo

    Southwest Jiaotong University, China

    8 publications · 262 citations

    8

    h-index

  4. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    8 publications · 146 citations

    8

    h-index

  5. WG
    Wanfu Gao

    Jilin University, China

    7 publications · 436 citations

    7

    h-index

1–5 of 1.8K

Venues

  1. Knowledge-Based Systems

    20 publications · 1.3K citations

    18

    h-index

  2. Neurocomputing

    15 publications · 3K citations

    14

    h-index

  3. Expert Systems with Applications

    17 publications · 904 citations

    14

    h-index

  4. Pattern Recognition

    15 publications · 692 citations

    13

    h-index

  5. Information Sciences

    12 publications · 565 citations

    12

    h-index

1–5