Date: | 2022, June 14 |
Time: | 10:30 a. m. |
Place: | G29-018 |
Author: | Hastings, Janna |
Title: | ESC-Rules: Explainable, Semantically Constrained Rule Sets for Prediction of Behavioural Intervention Outcomes |
ESC-Rules is a novel approach to explainable prediction of a continuous variable based on learning fuzzy weighted rules. The 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.