Rank-based Fusion

Rank-Based Fusion is a decision-level fusion strategy that combines ranked lists of candidate outputs from multiple models or sources by leveraging their relative positions to produce a consolidated ranking.

94

Publications

5.8K

Citations

236

Authors

116

Institutions

Publications per year

2017–2026

34

Variants

Rank Aggregation

Authors

236

  1. ML
    Marina L. Gavrilova

    University of Calgary, Canada

    9 publications · 558 citations

    9

    h-index

  2. DF
    D. Frank Hsu

    Fordham University, United States

    3 publications · 210 citations

    3

    h-index

  3. AK
    Ajay Kumar

    Hong Kong Polytechnic University, Hong Kong SAR China

    3 publications · 245 citations

    3

    h-index

  4. PC
    Pablo Castells

    Universidad Autónoma de Madrid, Spain

    2 publications · 77 citations

    2

    h-index

  5. PP
    Paul Pu Liang

    Carnegie Mellon University, United States

    2 publications · 1.1K citations

    2

    h-index

1–5 of 236

Venues

  1. IEEE Transactions on Image Processing

    3 publications · 159 citations

    3

    h-index

  2. Information Fusion

    3 publications · 145 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 152 citations

    3

    h-index

  4. Current Medicinal Chemistry

    2 publications · 28 citations

    2

    h-index

  5. IEEE Access

    2 publications · 174 citations

    2

    h-index

1–5