Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| SVM univarié semi-supervisé× | Isolation Forest× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2001–2004 | 2008 |
| Auteur d'origine≠ | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Type≠ | Semi-supervised anomaly / novelty detection | Unsupervised ensemble (random partitioning trees) |
| Source fondatrice≠ | Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Alias≠ | SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Apparentées | 5 | 5 |
| Résumé≠ | Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline. | 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. |
| ScholarGateJeu de données ↗ |
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