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.

本文提出了一种基于学习模糊加权规则的可解释连续变量预测新方法，采用符号和非符号混合方法和领域知识约束来实现透明可解释性，并在预测吸烟戒断行为干预结果研究中展示出比其他可解释方法更好的性能。