Σύγκριση μεθόδων

	Στοιβωτή Γενίκευση	Σύνολο Ενίσχυσης	Πλειοψηφική Ψηφοφορία
Πεδίο	Μάθηση Συνόλων Μοντέλων (Ensemble)	Μάθηση Συνόλων Μοντέλων (Ensemble)	Μάθηση Συνόλων Μοντέλων (Ensemble)
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1992	1990	1996
Δημιουργός	David Wolpert	Robert Schapire	Leo Breiman
Τύπος	meta-learning aggregation	sequential ensemble	voting aggregation
Θεμελιώδης πηγή	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Εναλλακτικές ονομασίες	stacking, meta-learning	adaptive boosting, sequential ensemble	hard voting
Συναφείς	3	4	5
Σύνοψη	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.	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.	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.