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| Wykrywanie anomalii za pomocą przyrostowego autoenkodera× | Isolation Forest× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2010s–present | 2008 |
| Twórca≠ | Various (online/incremental deep learning community) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Typ≠ | Online unsupervised anomaly detection | Unsupervised ensemble (random partitioning trees) |
| Źródło pierwotne≠ | An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Inne nazwy≠ | incremental autoencoder anomaly detection, streaming autoencoder anomaly detection, online AE anomaly detection, continual autoencoder anomaly detection | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learning, making it suitable for real-time or high-volume streaming scenarios where batch retraining is impractical. | 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. |
| ScholarGateZbiór danych ↗ |
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