手法を比較

	アンサンブルワンクラスドラボーマシン (Ensemble One-Class SVM)	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	2001	2008
提唱者	Tax, D. M. J. & Duin, R. P. W. (ensemble OC classifiers); Scholkopf et al. (OC-SVM base)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
種類	Ensemble anomaly detector	Unsupervised ensemble (random partitioning trees)
原典	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
別名	Ensemble OC-SVM, multiple one-class SVM, OC-SVM ensemble, one-class SVM committee	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
関連	4	5
概要	Ensemble One-Class SVM combines multiple one-class support vector machine models — each trained on a different random subset of the data or features — and aggregates their anomaly scores. By pooling several OC-SVM boundary estimates, the ensemble reduces the sensitivity to kernel choice and data sampling that afflicts a single one-class SVM, producing a more stable and accurate novelty or outlier detector.	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.