Hybrid Feature Selection

Hybrid Feature Selection is a feature selection strategy that combines multiple distinct paradigms—such as filter, wrapper, and embedded methods—within a unified framework to leverage their complementary strengths in identifying optimal feature subsets.

1.2K

Publications

72.5K

Citations

3.9K

Authors

1.4K

Institutions

Publications per year

2017–2026

829

Authors

3.9K

  1. RS
    Ram Sarkar

    Jadavpur University, India

    11 publications · 622 citations

    11

    h-index

  2. SM
    Seyedali Mirjalili

    Torrens University Australia, Australia

    11 publications · 2.6K citations

    11

    h-index

  3. PK
    Pawan Kumar Singh

    Jadavpur University, India

    10 publications · 470 citations

    10

    h-index

  4. AT
    Ahmad Taher Azar

    Benha University, Egypt

    7 publications · 655 citations

    7

    h-index

  5. ŞD
    Şengül Doğan

    Fırat University, Türkiye

    6 publications · 211 citations

    6

    h-index

1–5 of 3.9K

Venues

  1. IEEE Access

    81 publications · 7.6K citations

    41

    h-index

  2. Expert Systems with Applications

    39 publications · 4.9K citations

    33

    h-index

  3. Applied Soft Computing

    25 publications · 3K citations

    22

    h-index

  4. Neurocomputing

    19 publications · 2.8K citations

    17

    h-index

  5. Knowledge-Based Systems

    18 publications · 1.7K citations

    17

    h-index

1–5