Voting Ensembles

Voting Ensembles is a machine learning technique that combines the predictions of multiple base models through majority or weighted voting to improve generalization and robustness over individual classifiers.

453

Publications

25.7K

Citations

1.3K

Authors

629

Institutions

Publications per year

2017–2026

242

Variants

Ensemble Voting

Authors

1.3K

  1. FH
    Farhan Hassan Khan

    National University of Sciences and Technology, Pakistan

    6 publications · 345 citations

    6

    h-index

  2. LI
    Ludmila I. Kuncheva

    Bangor University, United Kingdom

    6 publications · 976 citations

    6

    h-index

  3. SB
    Saba Bashir

    National University of Sciences and Technology, Pakistan

    6 publications · 345 citations

    6

    h-index

  4. UQ
    Usman Qamar

    National University of Sciences and Technology, Pakistan

    5 publications · 292 citations

    5

    h-index

  5. LH
    Lawrence Hall

    University of South Florida, United States

    4 publications · 390 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. Expert Systems with Applications

    21 publications · 1.9K citations

    20

    h-index

  2. Neurocomputing

    15 publications · 758 citations

    13

    h-index

  3. Pattern Recognition

    12 publications · 1.9K citations

    11

    h-index

  4. arXiv (Cornell University)

    Repository · 10 publications · 245 citations

    10

    h-index

  5. Information Fusion

    9 publications · 1.3K citations

    9

    h-index

1–5