Machine learningMachine learning
鲁棒隔离森林
鲁棒隔离森林（Robust Isolation Forest）在经典的隔离森林（Isolation Forest）异常检测器基础上进行了扩展，引入了能够降低对数据污染、掩码效应和有偏随机分裂敏感性的策略。通过整合鲁棒性机制——例如改进的子采样、对可疑区域的重加权或偏差校正分裂——即使训练数据本身包含相当比例的异常值，或者特定的特征分布导致标准iForest产生不可靠的路径长度，它也能获得更可靠的异常分数。
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来源
- Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE. DOI: 10.1109/ICDM.2008.17 ↗
- Hariri, S., Kind, M. C., & Brunner, R. J. (2019). Extended Isolation Forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479–1489. DOI: 10.1109/TKDE.2019.2947676 ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Isolation Forest (Anomaly Detection with Robustness to Noise and Contamination). ScholarGate. https://scholargate.app/zh/machine-learning/robust-isolation-forest
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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