Online Ensemble Learning
Online Ensemble Learning is an ensemble learning paradigm that incrementally updates a dynamic set of base learners in real-time as new data instances arrive sequentially, distinguishing itself from batch ensemble methods by its ability to adapt to non-stationary data distributions with limited computational resources and memory.
205
Publications
17K
Citations
630
Authors
292
Institutions
Publications per year
2017–2026
122
205
Albert Bifet, Geoffrey Holmes, Richard Kirkby et al. · Research Commons (University of Waikato) · 2010 · 1.1K citations · Full text
BifetAlbert, HolmesGeoff, KirkbyRichard et al. · Journal of Machine Learning Research · 2010 · 776 citations
1–5 of 205
630
7
h-index
7
h-index
5
h-index
4
h-index
4
h-index
1–5 of 630
292
Nanyang Technological University
Singapore, Singapore
8 h-index
18 publications
2.1K citations

University of Birmingham
Birmingham, United Kingdom
8 h-index
18 publications
3.9K citations

Universidade Federal de Pernambuco
Recife, Brazil
7 h-index
13 publications
922 citations
Virginia Commonwealth University
Richmond, United States
4 h-index
8 publications
598 citations

École de Technologie Supérieure
Montreal, Canada
4 h-index
7 publications
1.1K citations

National University of Defense Technology
Changsha, China
4 h-index
11 publications
574 citations

Northeastern University
Shenyang, China
4 h-index
16 publications
544 citations

Johannes Kepler University of Linz
Linz, Austria
3 h-index
3 publications
199 citations
1–8 of 292
8 publications · 798 citations
8
h-index
TKDE · 7 publications · 1.7K citations
7
h-index
7 publications · 478 citations
7
h-index
6 publications · 787 citations
6
h-index
Repository · 6 publications · 181 citations
6
h-index
1–5