Compara mètodes

	Agregació de Borda	Generalització apilada
Camp	Aprenentatge per conjunts	Aprenentatge per conjunts
Família	Machine learning	Machine learning
Any d'origen	1781	1992
Autor original	Jean-Charles de Borda	David Wolpert
Tipus	rank-based aggregation	meta-learning aggregation
Font seminal	Borda, J. C. de (1781). Mémoire sur les élections au scrutin. Histoire de l'Académie Royale des Sciences.	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.
Àlies	weighted voting, rank aggregation	stacking, meta-learning
Relacionats	3	3
Resum	Borda count is a preference aggregation method that combines ranked predictions from multiple classifiers by assigning points based on ranking position. Each classifier ranks the possible outcomes, and each class receives points inversely proportional to its rank position. The class with the highest total score is selected. Originally proposed by French mathematician Jean-Charles de Borda in 1781, this method has been adapted for ensemble learning to aggregate soft predictions and rank-ordered outputs.	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.