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.
@article{arxiv.2208.12523,
title = {ESC-Rules: Explainable, Semantically Constrained Rule Sets},
author = {Martin Glauer and Robert West and Susan Michie and Janna Hastings},
journal= {arXiv preprint arXiv:2208.12523},
year = {2022}
}