方法证据记录
Ensemble Isolation Forest
Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method reduces the variance inherent in any single forest and yields more reliable outlier detection on complex or high-dimensional data.
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。
Ensemble Isolation Forest (Meta-Ensemble Anomaly Detection)
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 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE. · DOI 10.1109/ICDM.2008.17
- Isolation Forest. Wikipedia. · URL
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