Feature Selection

Feature Selection is a dimensionality reduction technique that identifies and retains the most relevant subset of input variables by evaluating their contribution to predictive accuracy, thereby distinguishing it from other feature transformation or extraction methods.

17.9K

Publications

1.2M

Citations

43.3K

Authors

6.8K

Institutions

Publications per year

2017–2026

9.4K

Authors

43.3K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    83 publications · 8.6K citations

    46

    h-index

  2. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    80 publications · 10.5K citations

    45

    h-index

  3. SM
    Seyedali Mirjalili

    Torrens University Australia, Australia

    53 publications · 9.3K citations

    42

    h-index

  4. MZ
    Mengjie Zhang

    Victoria University of Wellington, New Zealand

    66 publications · 9.1K citations

    41

    h-index

  5. QH
    Qinghua Hu

    Tianjin University, China

    42 publications · 5.2K citations

    33

    h-index

1–5 of 43.3K

Venues

  1. Expert Systems with Applications

    409 publications · 39.9K citations

    103

    h-index

  2. Pattern Recognition

    292 publications · 24.5K citations

    88

    h-index

  3. IEEE Access

    515 publications · 29.9K citations

    85

    h-index

  4. Knowledge-Based Systems

    260 publications · 21.4K citations

    82

    h-index

  5. Applied Soft Computing

    211 publications · 19.1K citations

    73

    h-index

1–5