手法を比較

	One-Class SVM	アイソレーションフォレスト	局所外れ値因子 (LOF)
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1999–2001	2008	2000
提唱者	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
種類	Anomaly / novelty detection (unsupervised)	Unsupervised ensemble (random partitioning trees)	Density-based anomaly detection (unsupervised)
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.	Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.
別名	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	LOF, local outlier factor, density-based outlier detection, local density deviation
関連	3	5	4
概要	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.	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.	Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space.