Machine learningMachine learning
自监督隔离森林
自监督隔离森林通过引入自监督预训练阶段来增强经典的隔离森林异常检测器。通过解决一个无需标签的代理任务——例如预测旋转、掩码特征或对比对——来学习更丰富的特征表示，然后将其用于构建隔离树，从而在复杂、高维的表格数据上产生更尖锐的异常分数。
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来源
- Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422. DOI: 10.1109/ICDM.2008.17 ↗
- Isolation Forest. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Isolation Forest (SSL-augmented Anomaly Detection). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-isolation-forest
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