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cs.LG2026
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
Udo Schlegel, Julian Rakuschek, Thomas Seidl +3
Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily…
cs.LG2026
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
Pernille Matthews, Lena Krieger, Tommaso Amico +3
Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they are not directly applicable…
cs.LG2026
What-If Explanations Over Time: Counterfactuals for Time Series Classification
Udo Schlegel, Thomas Seidl
Counterfactual explanations emerge as a powerful approach in explainable AI, providing what-if scenarios that reveal how minimal changes to an input time series can alter the model…