方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 可解释隔离森林× | 孤立森林 (Isolation Forest)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008 / 2017 | 2008 |
| 提出者≠ | 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. |
| 类型≠ | Anomaly detection with post-hoc explainability | Unsupervised ensemble (random partitioning trees) |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|