ESC-Rules: Explainable, Semantically Constrained Rule Sets
ESC-Rules: Explainable, Semantically Constrained Rule Sets
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ESC-Rules：可解释的、语义约束的规则集
DOI：
10.48550/arxiv.2208.12523
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Janna Hastings
中科院分区：
文献类型：
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作者：
Martin Glauer;Robert West;S. Michie;Janna Hastings
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小化基于本体的“语义损失”函数，包括用户指定的对应该学习的规则的约束，以便从用户的角度最大化结果规则集的可解释性。该系统将定量子符号学习与符号学习和基于领域知识的约束融合在一起。我们通过一个案例研究来说明我们的系统，用于预测戒烟行为干预的结果，并表明它优于其他可解释的方法，实现了接近深度学习模型的性能，同时提供透明的可解释性，这是健康领域决策者的基本要求。
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.
DOI：
10.1609/aaai.v35i5.16555
发表时间：
2021-03
期刊：
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影响因子：
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作者：
Litao Qiao;Weijia Wang;Bill Lin
通讯作者：
Litao Qiao;Weijia Wang;Bill Lin
DOI：
10.24963/ijcai.2022/767
发表时间：
2022-05
期刊：
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影响因子：
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作者：
Eleonora Giunchiglia;Mihaela C. Stoian;Thomas Lukasiewicz
通讯作者：
Eleonora Giunchiglia;Mihaela C. Stoian;Thomas Lukasiewicz