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	Robust Isolation Forest	Isolation Forest
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2008–2019	2008
Twórca	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Robust ensemble anomaly detection	Unsupervised ensemble (random partitioning trees)
Źródło pierwotne	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Inne nazwy	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Pokrewne	5	5
Podsumowanie	Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.