विधियों की तुलना करें

	एन्सेम्बल वन-क्लास एसवीएम	आइसोलेशन फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2001	2008
प्रवर्तक	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.
प्रकार	Ensemble anomaly detector	Unsupervised ensemble (random partitioning trees)
मौलिक स्रोत	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.
उपनाम	Ensemble OC-SVM, multiple one-class SVM, OC-SVM ensemble, one-class SVM committee	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
संबंधित	4	5
सारांश	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.