Self-supervised Isolation Forest
Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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. · URL
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
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Related methods
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