Training a random forest to discriminate real data from uniform synthetic data yields distances that stretch the boundary of the data, improving unsupervised anomaly detection accuracy on ADBench benchmarks.
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Explainable Unsupervised Anomaly Detection with Random Forest
Training a random forest to discriminate real data from uniform synthetic data yields distances that stretch the boundary of the data, improving unsupervised anomaly detection accuracy on ADBench benchmarks.