手法を比較

	ロバスト投票アンサンブル	バギング（ブートストラップ集約）	ブースティング
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2000s–2010s	1996	1990–1997
提唱者	Dietterich, T. G. (ensemble voting foundations); robustification extensions developed broadly in the ML community	Breiman, L.	Schapire, R. E.; Freund, Y.
種類	Robust ensemble aggregation	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)
原典	Dietterich, T. G. (2000). Ensemble methods in machine learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems, LNCS 1857, 1–15. Springer.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
別名	robust majority voting, robust vote aggregation, noise-tolerant voting ensemble, fault-tolerant classifier combination	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
関連	6	5	6
概要	Robust Voting Ensemble combines predictions from multiple base classifiers using noise-tolerant aggregation — such as weighted voting, trimmed voting, or median-based combination — to produce final decisions that remain reliable when individual classifiers are corrupted by noisy labels, adversarial inputs, or distributional shift.	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.