Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| One-class SVM× | Автокодувальник для виявлення аномалій× | Ізоляційний ліс× | |
|---|---|---|---|
| Галузь | Машинне навчання | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning | Machine learning |
| Рік появи≠ | 1999–2001 | 2006–2014 | 2008 |
| Автор методу≠ | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. | Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Anomaly / novelty detection (unsupervised) | Unsupervised deep learning (reconstruction-based) | Unsupervised ensemble (random partitioning trees) |
| Основоположне джерело≠ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ | Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Інші назви≠ | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM | AE anomaly detection, reconstruction-error anomaly detection, deep autoencoder outlier detection, unsupervised autoencoder anomaly detection | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Пов'язані≠ | 3 | 3 | 5 |
| Підсумок≠ | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. | Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records. | 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. |
| ScholarGateНабір даних ↗ |
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