Semi-supervised Ensemble Learning
Semi-Supervised Ensemble Learning is an ensemble learning paradigm that integrates both labeled and unlabeled data within a multi-model framework to improve predictive performance by leveraging the structural patterns inferred from the unlabeled data while maintaining the supervised learning objective on the labeled subset.
169
Publications
8.5K
Citations
657
Authors
248
Institutions
Publications per year
2017–2026
100
Parents
169
A Survey on Multiview Clustering
Guoqing Chao, Shiliang Sun, Jinbo Bi · IEEE Transactions on Artificial Intelligence · 2021 · 398 citations · Full text
Learning with Pseudo-Ensembles
Phil Bachman, Ouais Alsharif, Doina Precup · arXiv (Cornell University) · 2014 · 361 citations · Full text
MEKA: A multi-label/multi-target extension to Weka
Jesse Read · Aaltodoc (Aalto University) · 2025 · 217 citations · Full text
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South China University of Technology
Guangzhou, China
12 h-index
48 publications
3.4K citations

City University of Hong Kong
Hong Kong, Hong Kong
9 h-index
9 publications
640 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
9 h-index
11 publications
856 citations
University of Patras
Pátrai, Greece
7 h-index
12 publications
839 citations
Technological Educational Institute of Western Greece
Pátrai, Greece
7 h-index
15 publications
545 citations
Nanjing University
Nanjing, China
6 h-index
10 publications
431 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
12 publications
342 citations

Southern University of Science and Technology
Shenzhen, China
5 h-index
11 publications
459 citations
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6 publications · 248 citations
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Repository · 5 publications · 463 citations
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5 publications · 284 citations
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TKDE · 5 publications · 395 citations
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4 publications · 147 citations
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