ESC-Rules: Explainable, Semantically Constrained Rule Sets

Martin Glauer; Robert West; Susan Michie; Janna Hastings

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arXiv · 2208.12523

ESC-Rules: Explainable, Semantically Constrained Rule Sets

Martin Glauer·Robert West·Susan Michie·Janna Hastings

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Abstract

We describe a novel approach to explainable prediction of a continuous variable based on learning fuzzy weighted rules. Our model trains a set of weighted rules to maximise prediction accuracy and minimise an ontology-based 'semantic loss' function including user-specified constraints on the rules that should be learned in order to maximise the explainability of the resulting rule set from a user perspective. This system fuses quantitative sub-symbolic learning with symbolic learning and constraints based on domain knowledge. We illustrate our system on a case study in predicting the outcomes of behavioural interventions for smoking cessation, and show that it outperforms other interpretable approaches, achieving performance close to that of a deep learning model, while offering transparent explainability that is an essential requirement for decision-makers in the health domain.

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BibTeX RIS

Martin Glauer, Robert West, Susan Michie, Janna Hastings. 2022-08-26. ESC-Rules: Explainable, Semantically Constrained Rule Sets. https://arxiv.org/abs/2208.12523

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