论文标题
ESC-RULES：可解释的，语义上约束的规则集
ESC-Rules: Explainable, Semantically Constrained Rule Sets
论文作者
论文摘要
我们描述了一种基于学习模糊加权规则的连续变量的可解释预测的新方法。我们的模型训练一组加权规则，以最大化预测准确性，并最大程度地减少基于本体的“语义损失”功能，包括对规则的用户指定的约束，以最大程度地从用户的角度来实现所得规则的解释性。该系统将定量的亚符号学习与符号学习和基于领域知识的约束结合在一起。我们在一个案例研究中说明了我们的系统，以预测戒烟行为干预的结果，并表明它表现优于其他可解释的方法，实现了与深度学习模型接近的绩效，同时提供透明的可解释性，这是健康领域决策者的必要要求。
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.
