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
Parents
94
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors
Zhun Liu, Ying Shen, Varun Lakshminarasimhan et al. · 2018 · 993 citations · Full text
FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms
Yi Peng, Gang Kou, Wang Guoxun et al. · Omega · 2011 · 212 citations
M. Elena Renda, Umberto Straccia · 2003 · 192 citations
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116

University of Calgary
Calgary, Canada
11 h-index
29 publications
1.6K citations
University of Technology Sydney
Sydney, Australia
4 h-index
6 publications
270 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
12 publications
1.5K citations

Jadavpur University
Kolkata, India
3 h-index
9 publications
1K citations
Tsinghua University
Beijing, China
3 h-index
7 publications
191 citations
Microsoft (United States)
Redmond, United States
2 h-index
2 publications
102 citations

Universidade Nova de Lisboa
Lisbon, Portugal
2 h-index
5 publications
257 citations

Indian Institute of Technology Madras
Chennai, India
2 h-index
4 publications
109 citations
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3 publications · 159 citations
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3 publications · 145 citations
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Repository · 3 publications · 152 citations
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2 publications · 28 citations
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2 publications · 174 citations
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