Monotonic anomaly detection

Abstract:Semi-supervised anomaly detection is based on the principle that any record that looks different from normal training data is a potential anomaly. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). For distance-based methods, we propose an asymmetrical distance measure that takes this monotonicity into account by incorporating the ramp function. For the Isolation Forest algorithm, we propose a modified path length algorithm. Through experiments on synthetic and real-life datasets, we show that these proposals increase anomaly detection performance on datasets with monotonic attributes.

Machine Learning (cs.LG)

arXiv:2410.23158 [cs.LG]

(or arXiv:2410.23158v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2410.23158 Focus to learn more arXiv-issued DOI via DataCite

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