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arXiv Machine Learning By Mark Daniel Szalai, Gabor Horvath

RIFT: Relative Isolation From Trees For Anomaly Detection

Read the original on arXiv Machine Learning →

RIFT (Relative Isolation From Trees) is a deterministic anomaly detection method that builds a minimum spanning tree and scores points by the sum of apparent edge sizes from each point. For one-dimensional data, RIFT reproduces the closed-form limit of Isolation Forest exactly, while in higher dimensions it offers a parameter‑free, robust alternative that avoids axis‑parallel artifacts and handles varying density and clustered anomalies. An ensemble variant is proposed for large datasets, and experiments on synthetic data and the ADBench benchmark show comparable accuracy to Isolation Forest with reduced variance across random seeds.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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