방법 비교

	온라인 아이솔레이션 포레스트 (Online Isolation Forest)	온라인 랜덤 포레스트
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2008–2011	2009
창시자	Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al.	Saffari, A. et al.
유형	Streaming anomaly detection (online ensemble)	Incremental ensemble (streaming decision trees)
원전	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.
별칭	streaming isolation forest, incremental isolation forest, online iForest, adaptive isolation forest	ORF, streaming random forest, incremental random forest, adaptive random forest
관련	6	6
요약	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.