Monotonic anomaly detection
cs.LG
Submitted: 2024-10-30
Updated: 2026-09-01
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Semi-supervised anomaly detection is based on the principle that any record that looks different from normal training data is a potential anomaly.
Terminology
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.
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