Interpreting Classifiers through Attribute Interactions in Datasets

Andreas Henelius, Kai Puolamäki, Antti Ukkonen

Jun 16, 2017 (modified: Jun 19, 2017) ICML 2017 WHI Submission readers: everyone
  • Abstract: 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.
  • TL;DR: We present the novel ASTRID method for investigating which attribute interactions in the data classifiers exploit when making predictions.
  • Keywords: attribute interactions, constrained randomisation, classifiers, interpretability

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