29 citations · 54 across the 14 of their papers we have counts for
5 papers · 1 filter
Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
Szymon Bobek, Łukasz Bałec, Grzegorz J. Nalepa
Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a desired outcome. However, existi…
Financial Management System for SMEs: Real-World Deployment of Accounts Receivable and Cash Flow Prediction
Bartłomiej Małkus, Szymon Bobek, Grzegorz J. Nalepa
Small and Medium Enterprises (SMEs), particularly freelancers and early-stage businesses, face unique financial management challenges due to limited resources, small customer bases…
ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts
Bartłomiej Małkus, Szymon Bobek, Grzegorz J. Nalepa
Time series data is one of the most popular data modalities in critical domains such as industry and medicine. The demand for algorithms that not only exhibit high accuracy but als…
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…
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…