article Open AccessTop 1% cited
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
Machine Learning · 2000 · Vol. 40(2) · pp. 139–157
Thomas G. Dietterich✉(Oregon State University)
Machine Learning and Data ClassificationImbalanced Data Classification TechniquesNeural Networks and ApplicationsBoosting (machine learning)Decision treeMachine learningArtificial intelligenceEnsemble learningComputer scienceBootstrap aggregatingGradient boostingTraining setRandom forest
Funding
- National Science Foundation
Citations
2,926
FWCI
93.83
field-weighted impact
References
23
Percentile
100%
vs. same field & year
Citations per year
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References
Heuristics of instability and stabilization in model selection
The Annals of Statistics · 1996 · 1,152 citations
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Machine Learning · 1999 · 2,628 citations
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Neural Computation · 1998 · 3,564 citations
Bagging Predictors
Machine Learning · 1996 · 16,689 citations
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