DEM distills XGBoost into a residual decision tree with a new fidelity metric for interpretable anomaly detection in WBAN data, reporting AUC 0.9964 and 0.9047 with 0.17ms inference.
, author Ramalingam, S
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DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
DEM distills XGBoost into a residual decision tree with a new fidelity metric for interpretable anomaly detection in WBAN data, reporting AUC 0.9964 and 0.9047 with 0.17ms inference.