SHAP attribution profiles can identify complementary anomaly detectors whose divergence in explanations predicts non-overlapping detections, enabling stronger ensembles when high individual performance is maintained.
The need for unsupervised outlier model se lection: A review and evaluation of internal evaluation strategies
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Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity
SHAP attribution profiles can identify complementary anomaly detectors whose divergence in explanations predicts non-overlapping detections, enabling stronger ensembles when high individual performance is maintained.