Interpretable Hybrid Machine Learning Models Using FOLD-R++ and Answer Set Programming

TL;DR

This paper presents a hybrid machine learning approach combining Answer Set Programming with traditional classifiers to improve accuracy and interpretability in high-stakes domains like healthcare.

Contribution

It introduces a novel integration of ASP-derived rules with ML models, enhancing both prediction accuracy and interpretability.

Findings

Significant improvements in accuracy and F1 score on medical datasets

Enhanced interpretability through human-readable logical rules

Effective correction of uncertain predictions using symbolic reasoning

Abstract

Machine learning (ML) techniques play a pivotal role in high-stakes domains such as healthcare, where accurate predictions can greatly enhance decision-making. However, most high-performing methods such as neural networks and ensemble methods are often opaque, limiting trust and broader adoption. In parallel, symbolic methods like Answer Set Programming (ASP) offer the possibility of interpretable logical rules but do not always match the predictive power of ML models. This paper proposes a hybrid approach that integrates ASP-derived rules from the FOLD-R++ algorithm with black-box ML classifiers to selectively correct uncertain predictions and provide human-readable explanations. Experiments on five medical reveal statistically significant performance gains in accuracy and F1 score. This study underscores the potential of combining symbolic reasoning with conventional ML to achieve high interpretability without sacrificing accuracy