Comparer des méthodes

	Forêt d'isolement en ensemble	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2008 (base); ensemble variants 2010s–present	1999–2001
Auteur d'origine	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base IF); ensemble extensions by multiple researchers	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Meta-ensemble anomaly detection	Anomaly / novelty detection (unsupervised)
Source fondatrice	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE.	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.
Alias	EIF ensemble, multi-isolation-forest, isolation forest ensemble, ensemble anomaly detection with isolation trees	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	5	3
Résumé	Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method reduces the variance inherent in any single forest and yields more reliable outlier detection on complex or high-dimensional data.	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.