Interpretable Hybrid Machine Learning Models Using FOLD-R++ and Answer Set Programming
This addresses the need for interpretable AI in critical applications like healthcare, offering an incremental improvement by combining existing symbolic and ML methods.
The paper tackled the problem of opaque high-performing machine learning models in high-stakes domains like healthcare by proposing a hybrid approach that integrates Answer Set Programming-derived rules with black-box classifiers to correct uncertain predictions and provide explanations, resulting in statistically significant gains in accuracy and F1 score on five medical datasets.
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 datasets 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.