article Open AccessTop 1% cited
Random Forests
Machine Learning · 2001 · Vol. 45(1) · pp. 5–32
Leo Breiman✉(University of California, Berkeley)
Neural Networks and ApplicationsFace and Expression RecognitionMachine Learning and Data ClassificationRandom forestMathematicsAdaBoostStatisticsTree (set theory)GeneralizationSupport vector machineGeneralization errorMeasure (data warehouse)Artificial intelligence
Citations
121,242
FWCI
93.26
field-weighted impact
References
16
Percentile
100%
vs. same field & year
Citations per year
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References
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
Machine Learning · 2000 · 2,926 citations
Boosting the margin: a new explanation for the effectiveness of voting methods
The Annals of Statistics · 1998 · 2,317 citations
Arcing classifier (with discussion and a rejoinder by the author)
The Annals of Statistics · 1998 · 1,094 citations
The random subspace method for constructing decision forests
IEEE Transactions on Pattern Analysis and Machine Intelligence · 1998 · 6,773 citations
Shape Quantization and Recognition with Randomized Trees
Neural Computation · 1997 · 1,266 citations
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Machine Learning · 1999 · 2,628 citations
Bagging Predictors
Machine Learning · 1996 · 16,689 citations
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