Incremental Ensemble Learning is an ensemble learning paradigm in which base learners are sequentially added or updated over time to adapt to new data or tasks, thereby distinguishing it from static ensemble methods by its dynamic and cumulative model construction process.
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331
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178
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2017–2026
59
Publications
116
Dong Xibin, Zhiwen Yu, Wenming Cao et al. · Frontiers of Computer Science · 2019 · 1.9K citations
Computing, Data Science, Data Mining +11
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331
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178
Rowan University
Glassboro, United States
8 h-index
19 publications
3.4K citations
South China University of Technology
Guangzhou, China
7 h-index
23 publications
9.2K citations
City University of Hong Kong
Hong Kong, Hong Kong
6 h-index
7 publications
2.5K citations
Chinese Academy of Sciences
Beijing, China
5 h-index
8 publications
316 citations
Hong Kong Polytechnic University
Hong Kong, Hong Kong
4 h-index
8 publications
628 citations
École de Technologie Supérieure
Montreal, Canada
4 h-index
9 publications
147 citations
University of Windsor
Windsor, Canada
3 h-index
7 publications
80 citations
Shenzhen University
Shenzhen, China
3 h-index
6 publications
215 citations
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- Knowledge-Based Systems
3 publications · 347 citations
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- IEEE Transactions on Knowledge and Data Engineering
TKDE · 4 publications · 720 citations
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- IEEE Transactions on Neural Networks and Learning Systems
4 publications · 562 citations
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- Information Sciences
4 publications · 152 citations
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- IEEE Access
3 publications · 73 citations
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