用逻辑规则提升黑箱模型可解释性，医疗预测更准更可信
Interpretable Hybrid Machine Learning Models Using FOLD-R++ and Answer Set Programming
- 结合FOLD-R++生成的逻辑规则与黑箱模型，仅修正不确定预测
- 在5个医疗数据集上准确率和F1值显著提升
- 适合需高可解释性的医疗、金融等高风险领域
机器学习在医疗等高风险领域至关重要，但神经网络等高性能模型常缺乏可解释性，限制了信任与应用。符号方法如答案集编程（ASP）虽能提供可读逻辑规则，但预测能力往往不足。本文提出一种混合方法，将FOLD-R++算法生成的ASP规则与黑箱机器学习分类器结合，仅对置信度低的预测进行修正，并生成人类可读解释。在五个医疗数据集上的实验显示，该方法在准确率和F1分数上均取得统计显著提升。研究证明，融合符号推理与传统机器学习，可在不牺牲精度的前提下实现高水平可解释性。
原文摘要 · Abstract (English)
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
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