Machine learning
Isolation Forest
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.
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Sources
- Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI: 10.1109/ICDM.2008.17 ↗
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Active learning Isolation forestActive learning One-class SVMAutoencoder Anomaly DetectionBayesian Autoencoder Anomaly DetectionBayesian one-class SVMEnsemble Autoencoder Anomaly DetectionEnsemble Isolation ForestEnsemble One-class SVMExplainable Autoencoder Anomaly DetectionExplainable Isolation ForestExplainable One-Class SVMLightGBMLocal Outlier FactorOne-class SVMOnline Autoencoder Anomaly DetectionOnline Isolation ForestOnline One-class SVMOut-of-Distribution DetectionRobust Autoencoder anomaly detectionRobust Gaussian Mixture ModelRobust Isolation forestRobust One-class SVMRobust Random ForestSelf-supervised Autoencoder Anomaly DetectionSelf-supervised Isolation ForestSelf-supervised One-class SVMSemi-supervised Autoencoder Anomaly DetectionSemi-supervised Isolation ForestSemi-supervised One-class SVM