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RLAX: A Reinforcement Learning-Based Framework for Explaining Anomalous Data
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DOI：10.1007/s10994-026-07182-z.png)
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Abstract
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Singling out the most informative features able to discriminate between normal and anomalous data is a formidable problem dealing with the enormous search space represented by the power set of the whole feature set. These features indeed encode the explanation of the deviating behavior associated with selected observations and represent the hidden knowledge of interest for the analyst in many applicative contexts, namely cybersecurity, bioinformatics, and green by AI, to cite a few. In this work, we present RLAX, a reinforcement learning-based algorithm detecting explanations from collections of high-dimensional data sets. The algorithm receives in input the unlabelled data collection of normal instances, a small set of labelled inlier examples, and a small set of anomalies to be explained. Separation between normal samples and anomalies is measured by means of a distance-based outlier score. The designed learning strategy thus induces a ranking among features that allows isolating those maximizing separation accuracy. Beyond an add-only formulation, we investigate a subspace refinement strategy that augments the action space with remove and stop actions, enabling non-monotone refinement and adaptive early termination. Experimental results highlight that the size of the explanation returned consists of a small fraction of the whole feature set, and that the associated accuracy and subspace margin are of remarkable quality. Additional analyses of the refinement strategy further clarify its impact on exploration efficiency and explanation quality.
Keywords:
Explainable AI
Anomaly explanation
Reinforcement learning
Feature selection
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