Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data

Philipp Röchner, Franz Rothlauf

Abstract: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.