Сравнение методов

	Онлайновые K-ближайших соседей	Online Random Forest
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2010s (formalized in streaming-learning literature)	2009
Автор метода	Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)	Saffari, A. et al.
Тип	Instance-based online classifier/regressor	Incremental ensemble (streaming decision trees)
Основополагающий источник	Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE.	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.
Другие названия	Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation	ORF, streaming random forest, incremental random forest, adaptive random forest
Связанные	5	6
Сводка	Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most recent and most representative examples to classify or predict each incoming point by proximity.	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.