Interpreting Classifiers through Attribute Interactions in Datasets
This work addresses interpretability in machine learning, particularly for applications like pharmacovigilance and bioinformatics, but appears incremental as it builds on existing concepts of attribute interactions.
The authors tackled the problem of interpreting classifiers by identifying which attribute interactions they exploit for predictions, and introduced the ASTRID method to reveal these interactions, demonstrating its utility empirically.
In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions. Attribute interactions in classification tasks mean that two or more attributes together provide stronger evidence for a particular class label. Knowledge of such interactions makes models more interpretable by revealing associations between attributes. This has applications, e.g., in pharmacovigilance to identify interactions between drugs or in bioinformatics to investigate associations between single nucleotide polymorphisms. We also show how the found attribute partitioning is related to a factorisation of the data generating distribution and empirically demonstrate the utility of the proposed method.