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arXiv Machine Learning By Oliver Urs Lenz, Matthijs van Leeuwen

Monotonic anomaly detection

Read the original on arXiv Machine Learning →

The paper introduces methods for monotonic anomaly detection, focusing on anomalies that exhibit high (or low) attribute values rather than arbitrary deviations. It proposes an asymmetrical distance measure using a ramp function for distance-based methods and a modified path length algorithm for Isolation Forest. Experiments on synthetic and real-life datasets demonstrate improved detection performance on datasets with monotonic attributes.

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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