Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Detekce anomálií pomocí semi-supervizovaného autoenkodéru× | Isolation Forest× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2018–2020 | 2008 |
| Tvůrce≠ | Ruff, L. et al.; Zong, B. et al. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Typ≠ | Semi-supervised deep anomaly detection | Unsupervised ensemble (random partitioning trees) |
| Původní zdroj≠ | Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020). link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Další názvy≠ | Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist. | 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. |
| ScholarGateDatová sada ↗ |
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