Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Forêt d'isolement semi-supervisée× | Isolation Forest× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2013–2020 | 2008 |
| Auteur d'origine≠ | Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020 | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Type≠ | Ensemble anomaly detection (semi-supervised extension) | Unsupervised ensemble (random partitioning trees) |
| Source fondatrice≠ | Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Alias≠ | SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection. | 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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