Comparer des méthodes

	Vote majoritaire	Vote Pondéré
Domaine	Apprentissage ensembliste	Prise de décision
Famille	Machine learning	MCDM
Année d'origine	1996	1951
Auteur d'origine	Leo Breiman	Arrow, K. J.
Type	voting aggregation	Social choice — weighted positional voting rule
Source fondatrice	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Arrow, K. J. (1951). Social Choice and Individual Values. Wiley, New York
Alias	hard voting	—
Apparentées	5	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.	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.