Machine learningMachine learning
主动学习孤立森林
主动学习孤立森林将孤立森林的无监督异常评分能力与一种迭代查询策略相结合，该策略要求人类专家标记最具信息量的实例。其结果是一种探测器，它使用最少的标记预算来精炼其异常边界，与纯粹的无监督基线相比，在罕见和细微的异常方面显著提高了精度。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422. DOI: 10.1109/ICDM.2008.17 ↗
如何引用本页
ScholarGate. (2026, June 3). Active Learning with Isolation Forest for Anomaly Detection. ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-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.
- 主动学习机器学习↔ compare
- 自动编码器异常检测机器学习↔ compare
- 孤立森林 (Isolation Forest)机器学习↔ compare
- 单类支持向量机机器学习↔ compare
- 半监督隔离森林机器学习↔ compare