方法证据记录
Robust Isolation forest
Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Robust Isolation Forest (Anomaly Detection with Robustness to Noise and Contamination)
分类方法记录 · ml-model / machine-learning
- 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
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