# ESC-Rules: Explainable, Semantically Constrained Rule Sets

arXiv: https://arxiv.org/abs/2208.12523 · HTML: https://mlsanity.com/paper/2208.12523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Post-hoc Interpretability for Neural NLP: A Survey](https://mlsanity.com/paper/2108.04840/) (2021)
- [Deep Learning with Logical Constraints](https://mlsanity.com/paper/2205.00523/) (2022)
- [Learning Accurate and Interpretable Decision Rule Sets from Neural Networks](https://mlsanity.com/paper/2103.02826/) (2021)
- [Post-hoc explanation of black-box classifiers using confident itemsets](https://mlsanity.com/paper/2005.01992/) (2020)
- [Differentiable Logic Machines](https://mlsanity.com/paper/2102.11529/) (2021)
- [Controlling Neural Networks with Rule Representations](https://mlsanity.com/paper/2106.07804/) (2021)
- [A parameterized activation function for learning fuzzy logic operations in deep neural networks](https://mlsanity.com/paper/1708.08557/) (2017)

## Built on by (most cited first, 1 of 1)

- [A fuzzy loss for ontology classification](https://mlsanity.com/paper/2405.02083/) (2024)

## Abstract (arXiv)

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.