Сравнение на методи
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| Обясним изолационен лес× | Isolation Forest× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2008 / 2017 | 2008 |
| Създател≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Anomaly detection with post-hoc explainability | Unsupervised ensemble (random partitioning trees) |
| Основополагащ източник≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Други названия≠ | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Свързани | 5 | 5 |
| Резюме≠ | Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains. | 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 от данни ↗ |
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