Compară metode

	Învățare activă cu ansambluri stratificate	Stacking
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1992–2012	1992
Autorul original	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Wolpert, D.H.
Tip	Hybrid (active learning + stacked ensemble)	Ensemble (heterogeneous meta-learning)
Sursa seminală	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Denumiri alternative	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Înrudite	5	5
Rezumat	Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.