AI RESEARCH
A Bayesian Framework for Uncertainty-Aware Explanations in Power Quality Disturbance Classification
arXiv CS.LG
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ArXi:2604.13658v1 Announce Type: new Advanced deep learning methods have shown remarkable success in power quality disturbance (PQD) classification. To enhance model transparency, explainable AI (XAI) techniques have been developed to provide instance-specific interpretations of classifier decisions. However, conventional XAI methods yield deterministic explanations, overlooking uncertainty and limiting reliability in safety-critical applications.