方法对比
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| 主动学习孤立森林× | 半监督隔离森林× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008–2019 | 2013–2020 |
| 提出者≠ | Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base) | Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020 |
| 类型≠ | Active learning wrapper over isolation forest anomaly detector | Ensemble anomaly detection (semi-supervised extension) |
| 开创性文献≠ | Das, S., Wong, W. K., Fern, A., Dietterich, T. G., & Amran Siddiqui, M. (2019). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), pp. 1009–1014. link ↗ | Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262. link ↗ |
| 别名 | AL-iForest, active anomaly detection with isolation forest, active isolation forest, query-guided isolation forest | SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest |
| 相关≠ | 5 | 6 |
| 摘要≠ | Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving precision on rare and subtle anomalies compared to a purely unsupervised baseline. | Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection. |
| ScholarGate数据集 ↗ |
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