Comparar métodos

	Generalización apilada	Votación Ponderada
Campo	Aprendizaje por conjuntos	Toma de decisiones
Familia	Machine learning	MCDM
Año de origen	1992	1951
Autor original	David Wolpert	Arrow, K. J.
Tipo	meta-learning aggregation	Social choice — weighted positional voting rule
Fuente seminal	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	stacking, meta-learning	—
Relacionados	3	0
Resumen	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.