Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Zelfgesuperviseerde One-class SVM× | Isolation Forest× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2018 | 2008 |
| Grondlegger≠ | Golan & El-Yaniv; Ruff et al. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Type≠ | Self-supervised anomaly/novelty detection | Unsupervised ensemble (random partitioning trees) |
| Oorspronkelijke bron≠ | Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Aliassen≠ | SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Verwant≠ | 6 | 5 |
| Samenvatting≠ | Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples. | 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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