Comparer des méthodes

	Généralisation empilée	Vote majoritaire
Domaine	Apprentissage ensembliste	Apprentissage ensembliste
Famille	Machine learning	Machine learning
Année d'origine	1992	1996
Auteur d'origine	David Wolpert	Leo Breiman
Type	meta-learning aggregation	voting aggregation
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Alias	stacking, meta-learning	hard voting
Apparentées	3	5
Résumé	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.	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.