Comparer des méthodes

	Vote majoritaire	Généralisation empilée
Domaine	Apprentissage ensembliste	Apprentissage ensembliste
Famille	Machine learning	Machine learning
Année d'origine	1996	1992
Auteur d'origine	Leo Breiman	David Wolpert
Type	voting aggregation	meta-learning aggregation
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.
Alias	hard voting	stacking, meta-learning
Apparentées	5	3
Résumé	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.	Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models.