Salīdzināt metodes

	Tiešsaistes izolācijas mežs (Online Isolation Forest)	Tiešsaistes nejaušais mežs
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2008–2011	2009
Autors	Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al.	Saffari, A. et al.
Tips	Streaming anomaly detection (online ensemble)	Incremental ensemble (streaming decision trees)
Pirmavots	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	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.
Citi nosaukumi	streaming isolation forest, incremental isolation forest, online iForest, adaptive isolation forest	ORF, streaming random forest, incremental random forest, adaptive random forest
Saistītās	6	6
Kopsavilkums	Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history. This makes it practical for real-time monitoring, fraud detection, and sensor-data surveillance where data volumes grow indefinitely.	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.