Methoden vergleichen

	Mehrheitsentscheid	Gestapelte Generalisierung	Gewichtete Abstimmung
Fachgebiet	Ensemble-Lernen	Ensemble-Lernen	Entscheidungsfindung
Familie	Machine learning	Machine learning	MCDM
Entstehungsjahr	1996	1992	1951
Urheber	Leo Breiman	David Wolpert	Arrow, K. J.
Typ	voting aggregation	meta-learning aggregation	Social choice — weighted positional voting rule
Wegweisende Quelle	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
Aliasnamen	hard voting	stacking, meta-learning	—
Verwandt	5	3	0
Zusammenfassung	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.