Consensus Feature Selection

Consensus Feature Selection is a feature selection strategy that identifies a stable subset of predictive variables by aggregating multiple feature rankings derived from heterogeneous data samples, models, or algorithms to enhance robustness and generalizability.

31

Publications

1.7K

Citations

133

Authors

75

Institutions

Publications per year

2017–2026

19

Variants

Stable Feature Selection

Authors

133

  1. ZC
    Zhiwen Cao

    Ningbo University, China

    3 publications · 176 citations

    3

    h-index

  2. XX
    Xijiong Xie

    Ningbo University, China

    3 publications · 176 citations

    3

    h-index

  3. MS
    Ming Shao

    Northeastern University, United States

    2 publications · 69 citations

    2

    h-index

  4. YF
    Yun Fu

    Northeastern University, United States

    2 publications · 69 citations

    2

    h-index

  5. LN
    Lingbo Nong

    First Affiliated Hospital of Guangzhou Medical University, China

    1 publications · 63 citations

    1

    h-index

1–5 of 133

Venues

  1. Pattern Recognition Letters

    3 publications · 133 citations

    3

    h-index

  2. Pattern Recognition

    2 publications · 93 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 81 citations

    2

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 73 citations

    2

    h-index

  5. IEEE Transactions on Cybernetics

    2 publications · 85 citations

    2

    h-index

1–5