Feature Selection For Regression

Feature Selection for Regression is a methodological framework within feature selection that specifically identifies and retains predictor variables most relevant to predicting continuous outcome variables in regression analysis, distinguishing it from feature selection tasks targeting classification or unsupervised learning.

239

Publications

26.4K

Citations

729

Authors

347

Institutions

Publications per year

2017–2026

128

Authors

729

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    5 publications · 262 citations

    5

    h-index

  2. XL
    Xuelong Li

    Northwestern Polytechnical University, China

    4 publications · 149 citations

    4

    h-index

  3. RH
    Rongyao Hu

    Guangxi Normal University, China

    3 publications · 67 citations

    3

    h-index

  4. IT
    Ioannis Tsamardinos

    University of Crete, Greece

    3 publications · 224 citations

    3

    h-index

  5. OS
    Omar Saber Qasim

    University of Mosul, Iraq

    3 publications · 127 citations

    3

    h-index

1–5 of 729

Venues

  1. Expert Systems with Applications

    11 publications · 610 citations

    11

    h-index

  2. Chemometrics and Intelligent Laboratory Systems

    7 publications · 1.3K citations

    7

    h-index

  3. Neurocomputing

    7 publications · 334 citations

    7

    h-index

  4. Journal of Chemometrics

    5 publications · 621 citations

    5

    h-index

  5. Knowledge-Based Systems

    5 publications · 323 citations

    5

    h-index

1–5