Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Isolation Forest× | Lokālā novirzes faktors (LOF)× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2008 | 2000 |
| Autors≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. |
| Tips≠ | Unsupervised ensemble (random partitioning trees) | Density-based anomaly detection (unsupervised) |
| Pirmavots≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. DOI ↗ |
| Citi nosaukumi≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | LOF, local outlier factor, density-based outlier detection, local density deviation |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space. |
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