Mutual Information-based Selection

Mutual Information-Based Selection is a feature selection method that identifies relevant features by measuring the mutual information between each feature and the target variable, thereby distinguishing itself from other filter methods through its reliance on information-theoretic dependence as the criterion for selection.

136

Publications

13.9K

Citations

376

Authors

170

Institutions

Publications per year

2017–2026

40

Variants

Mutual Information Feature Selection

Authors

376

  1. MV
    Michel Verleysen

    UCLouvain, Belgium

    8 publications · 656 citations

    8

    h-index

  2. JH
    Jinjie Huang

    Harbin University of Science and Technology, China

    3 publications · 405 citations

    3

    h-index

  3. WQ
    Wenbin Qian

    Jiangxi Agricultural University, China

    3 publications · 163 citations

    3

    h-index

  4. TW
    Tommy W. S. Chow

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 152 citations

    3

    h-index

  5. YC
    Yunze Cai

    Shanghai Jiao Tong University, China

    3 publications · 405 citations

    3

    h-index

1–5 of 376

Venues

  1. Neurocomputing

    13 publications · 893 citations

    13

    h-index

  2. Pattern Recognition

    9 publications · 1.1K citations

    9

    h-index

  3. Expert Systems with Applications

    7 publications · 933 citations

    7

    h-index

  4. Applied Intelligence

    4 publications · 380 citations

    4

    h-index

  5. 4

    h-index

1–5