Comparer des méthodes

	Robust Bagging	Robust Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996–2000s	1999–2001
Auteur d'origine	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Freund, Y.; Mason, L. et al.
Type	Ensemble (robust bootstrap aggregating)	Ensemble (robust sequential boosting)
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.
Alias	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
Apparentées	6	6
Résumé	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.