方法证据记录
Semi-supervised Isolation Forest
Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.
源记录
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Semi-supervised Isolation Forest for Anomaly Detection
分类方法记录 · ml-model / machine-learning
- Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262. · URL
- Isolation Forest. Wikipedia. · URL
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