Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Автоэнкодерная детекция аномалий на основе самообучения× | Isolation Forest× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2018–2020 | 2008 |
| Автор метода≠ | Golan & El-Yaniv; broader self-supervised anomaly detection community | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Unsupervised / self-supervised deep learning | Unsupervised ensemble (random partitioning trees) |
| Основополагающий источник≠ | Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Другие названия≠ | SSL Autoencoder anomaly detection, self-supervised reconstruction anomaly detection, pretext-task autoencoder anomaly detection, contrastive autoencoder anomaly detection | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Self-supervised autoencoder anomaly detection trains an autoencoder using self-supervised pretext tasks — such as predicting geometric transformations or solving jigsaw puzzles — on unlabeled normal data, then flags as anomalous any input whose reconstruction error or pretext-task score deviates substantially from the learned normal distribution. | 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. |
| ScholarGateНабор данных ↗ |
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