Anomaly explanation with random forests

doi:10.1016/j.eswa.2020.113187

Anomaly explanation with random forests

2020-07-01

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DOI：10.1016/j.eswa.2020.113187

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Anomaly detection has become an important topic in many domains with many different solutions proposed until now. Despite that, there are only a few anomaly detection methods trying to explain how the sample differs from the rest. This work contributes to filling this gap because knowing why a sample is considered anomalous is critical in many application domains. The proposed solution uses a specific type of random forests to extract rules explaining the difference, which are then filtered and presented to the user as a set of classification rules sharing the same consequent, or as the equivalent rule with an antecedent in a disjunctive normal form. The quality of that solution is documented by comparison with the state of the art algorithms on 34 real-world datasets. (C) 2020 Published by Elsevier Ltd.

Keywords:

Anomaly detection

Anomaly explanation

Classification rules

Feature selection

Random forests

Anomaly explanation with random forests

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