Bandingkan metode

	Ensemble Pemungutan Suara Daring	Voting Ensemble
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2001–2009	1990s–2004
Pencetus	Oza, N. C. & Russell, S.; extended by Bifet et al.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipe	Online ensemble (incremental majority vote)	Ensemble (combination of multiple classifiers by vote)
Sumber perintis	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	streaming voting ensemble, incremental voting ensemble, online majority-vote ensemble, data-stream voting classifier	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Terkait	6	5
Ringkasan	Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from scratch, making it well-suited to real-time classification tasks where data arrives sequentially and concept drift may occur.	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.