手法を比較

	アクティブラーニング・ワンクラス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	2000s	2008
提唱者	Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
種類	Semi-supervised anomaly/novelty detection with iterative labeling	Unsupervised ensemble (random partitioning trees)
原典	Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). 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	AL-OCSVM, active one-class SVM, active novelty detection SVM, query-driven OCSVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
関連	4	5
概要	Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort.	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.