Multi-label Ensemble Learning
Multi-Label Ensemble Learning is an ensemble learning paradigm that combines multiple base learners to address multi-label classification tasks, where each instance can be associated with a subset of simultaneously relevant labels, distinguishing it from traditional single-label ensemble methods by explicitly modeling label correlations and dependencies.
121
Publications
10.9K
Citations
379
Authors
184
Institutions
Publications per year
2017–2026
66
121
Improved boosting algorithms using confidence-rated predictions
Robert E. Schapire, Yoram Singer · 1998 · 2.6K citations
An extensive experimental comparison of methods for multi-label learning
Gjorgji Madjarov, Dragi Kocev, Dejan Gjorgjevikj et al. · Pattern Recognition · 2012 · 731 citations
Multi-label Classification Using Ensembles of Pruned Sets
Jesse Read, Bernhard Pfahringer, Geoffrey Holmes · 2008 · 429 citations · Full text
1–5 of 121
379
7
h-index
5
h-index
5
h-index
4
h-index
3
h-index
1–5 of 379
184
Nanjing University
Nanjing, China
5 h-index
11 publications
462 citations
Virginia Commonwealth University
Richmond, United States
5 h-index
7 publications
297 citations
University of Córdoba
Córdoba, Spain
5 h-index
12 publications
636 citations
Tongji University
Shanghai, China
5 h-index
14 publications
578 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
5 h-index
8 publications
257 citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
5 h-index
10 publications
257 citations

South China University of Technology
Guangzhou, China
4 h-index
13 publications
774 citations
Instituto Maimónides de Investigación Biomédica de Córdoba
Córdoba, Spain
4 h-index
9 publications
321 citations
1–8 of 184
6 publications · 1.1K citations
6
h-index
6 publications · 370 citations
6
h-index
5 publications · 309 citations
5
h-index
5 publications · 320 citations
5
h-index
Repository · 5 publications · 99 citations
5
h-index
1–5