4 papers
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…
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…
Know Your Streams: On the Conceptualization, Characterization, and Generation of Intentional Event Streams
Andrea Maldonado, Christian Imenkamp, Hendrik Reiter +4
The shift toward IoT-enabled, sensor-driven systems has transformed how operational data is generated, favoring continuous, real-time event streams (ES) over static event logs. Thi…
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…