手法を比較

	ロバストバギング	バギング（ブートストラップ集約）	ランダムフォレスト	ロバストブースティング
分野	機械学習	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning	Machine learning
提唱年	1996–2000s	1996	2001	1999–2001
提唱者	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Breiman, L.	Breiman, L.	Freund, Y.; Mason, L. et al.
種類	Ensemble (robust bootstrap aggregating)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (bagging of decision trees)	Ensemble (robust sequential boosting)
原典	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	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	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
関連	6	5	4	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.