Comparer des méthodes

	Vote majoritaire	Généralisation empilée	Vote Pondéré
Domaine	Apprentissage ensembliste	Apprentissage ensembliste	Prise de décision
Famille	Machine learning	Machine learning	MCDM
Année d'origine	1996	1992	1951
Auteur d'origine	Leo Breiman	David Wolpert	Arrow, K. J.
Type	voting aggregation	meta-learning aggregation	Social choice — weighted positional voting rule
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.	Arrow, K. J. (1951). Social Choice and Individual Values. Wiley, New York
Alias	hard voting	stacking, meta-learning	—
Apparentées	5	3	0
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.	WEIGHTED-VOTING (Weighted Voting — Weighted positional aggregation of multiple rankings) is a ranking multi-criteria decision-making (MCDM) method introduced by Arrow, K. J. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.