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cs.LG2024
Interpretable Graph Neural Networks for Heterogeneous Tabular Data
Amr Alkhatib, Henrik Boström
Many machine learning algorithms for tabular data produce black-box models, which prevent users from understanding the rationale behind the model predictions. In their unconstraine…
cs.LG2024★ 6 cited
Explaining Predictions by Characteristic Rules
Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis
Characteristic rules have been advocated for their ability to improve interpretability over discriminative rules within the area of rule learning. However, the former type of rule…