Compara mètodes

	Robust Bagging	Votació en conjunt
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1996–2000s	1990s–2004
Autor original	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipus	Ensemble (robust bootstrap aggregating)	Ensemble (combination of multiple classifiers by vote)
Font seminal	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Àlies	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Relacionats	6	5
Resum	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.