Methoden vergleichen

	Aktives Lernen mit Stacking-Ensemble	Voting Ensemble
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1992–2012	1990s–2004
Urheber	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Typ	Hybrid (active learning + stacked ensemble)	Ensemble (combination of multiple classifiers by vote)
Wegweisende Quelle	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Aliasnamen	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Verwandt	5	5
Zusammenfassung	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.