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| Detekcija anomalija pomoću ansambla autoenkodera× | Isolation Forest× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2017 | 2008 |
| Tvorac≠ | Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tip≠ | Ensemble unsupervised anomaly detection | Unsupervised ensemble (random partitioning trees) |
| Temeljni izvor≠ | Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier Detection with Autoencoder Ensembles. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 90–98. SIAM. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Drugi nazivi≠ | ensemble AE anomaly detection, autoencoder ensemble outlier detection, multi-autoencoder anomaly scoring, AE ensemble unsupervised anomaly detection | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Srodne | 5 | 5 |
| Sažetak≠ | Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random initialisation or suboptimal architecture choices. | 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. |
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