Author

F. Spinnato

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#artificial intelligence Preprint Sep 2026

A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification

Irregular time series, characterized by non-uniform sampling intervals, missing observations, and variable lengths, are ubiquitous in healthcare, mobility, and environmental monitoring, yet effective and interpretable classifiers for this setting are limited. Existing approaches often rely on imputation, which can obsc...

F. Spinnato · 0 citations

#artificial intelligence Preprint Sep 2026

A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods

Evaluating explainable Artificial Intelligence (XAI) methods is a challenging task due to the lack of reliable evaluation procedures and, in particular, the absence of ground truth explanations. In the literature, existing evaluation approaches typically assess explanations by measuring their fidelity with respect to t...

Miquel Miró-Nicolau, F. Spinnato, Riccardo Guidotti · 0 citations