手法を比較

	ロバスト・ランダム・フォレスト	アイソレーションフォレスト
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2000s–2010s	2008
提唱者	Various (extensions of Breiman 2001 Random Forest)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
種類	Robust Ensemble (noise-tolerant bagging of decision trees)	Unsupervised ensemble (random partitioning trees)
原典	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
別名	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
関連	6	5
概要	Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.	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.