3 papers
cs.LG2025
From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
Maciej Mozolewski, Betül Bayrak, Kerstin Bach +1
In eXplainable Artificial Intelligence (XAI), instance-based explanations for time series have gained increasing attention due to their potential for actionable and interpretable i…
cs.LG2025
Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions
Maciej Mozolewski, Szymon Bobek, Grzegorz J. Nalepa
Explaining machine learning (ML) models for time series (TS) classification remains challenging due to the difficulty of interpreting raw time series and the high dimensionality of…
cs.CY2024
Dataset resulting from the user study on comprehensibility of explainable AI algorithms
Szymon Bobek, Paloma Korycińska, Monika Krakowska +5
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were rec…