Σύγκριση μεθόδων

	Robust Isolation Forest	Isolation Forest
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2008–2019	2008
Δημιουργός	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Τύπος	Robust ensemble anomaly detection	Unsupervised ensemble (random partitioning trees)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Συναφείς	5	5
Σύνοψη	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.