Sammenlign metoder

	Online Voting Ensemble	Online Random Forest
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2001–2009	2009
Ophavsperson	Oza, N. C. & Russell, S.; extended by Bifet et al.	Saffari, A. et al.
Type	Online ensemble (incremental majority vote)	Incremental ensemble (streaming decision trees)
Oprindelig kilde	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.	Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.
Aliasser	streaming voting ensemble, incremental voting ensemble, online majority-vote ensemble, data-stream voting classifier	ORF, streaming random forest, incremental random forest, adaptive random forest
Relaterede	6	6
Resumé	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.	Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.