手法を比較

	ロバストバギング	ロバストブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	1996–2000s	1999–2001
提唱者	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Freund, Y.; Mason, L. et al.
種類	Ensemble (robust bootstrap aggregating)	Ensemble (robust sequential boosting)
原典	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.
別名	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
関連	6	6
概要	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.