Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Detección de anomalías con autoencoder semi-supervisado× | Isolation Forest× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2018–2020 | 2008 |
| Autor original≠ | Ruff, L. et al.; Zong, B. et al. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tipo≠ | Semi-supervised deep anomaly detection | Unsupervised ensemble (random partitioning trees) |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | 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 |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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