Extremely Randomized Trees is a tree-based ensemble learning method that distinguishes itself from other randomized tree algorithms by introducing additional randomness in both the selection of split points and the choice of features at each node, thereby reducing variance through more diverse tree structures.
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2017–2026
137
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163
The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent
Peter Bunting, Åke Rosenqvist, Richard Lucas et al. · Remote Sensing · 2018 · 690 citations · Full text
Natural Sciences, Ecology, Geography +15
Amir Saffari, Christian Leistner, Jakob Santner et al. · 2009 · 478 citations
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1–5 of 604
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290
University of Liège
Liège, Belgium
10 h-index
27 publications
1.8K citations
Centre National de la Recherche Scientifique
Paris, France
6 h-index
22 publications
1.4K citations
University of Maryland, College Park
College Park, United States
5 h-index
10 publications
4.8K citations
Université de Bordeaux
Bordeaux, France
4 h-index
8 publications
242 citations
California Institute of Technology
Pasadena, United States
4 h-index
8 publications
242 citations
University of Bonn
Bonn, Germany
4 h-index
4 publications
121 citations
Beijing Normal University
Beijing, China
4 h-index
25 publications
4.5K citations
Observatoire de la Côte d’Azur
Nice, France
4 h-index
11 publications
349 citations
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- Remote Sensing
8 publications · 1.2K citations
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6 publications · 424 citations
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5 publications · 193 citations
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- Astronomy and Astrophysics
3 publications · 107 citations
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- Applied Sciences
2 publications · 63 citations
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