提升机器学习解释的覆盖率与效率，确保正确性。
Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization
- 单步生成法一次性确定每个特征的上下界，避免迭代开销。
- 双步法逐步扩展解释范围，平均覆盖度提升72.60%。
- 适合需要高可靠性和广泛适用性的模型解释场景。
机器学习模型支持决策，但其预测依据不透明。清晰可靠的解释有助于用户做出明智判断，避免盲目信任模型输出。然而，许多现有解释方法无法保证正确性。基于逻辑的方法虽能确保正确性，但解释范围常过于受限。近期工作通过逐特征增量扩展解释，在保持正确性的同时提升覆盖。该过程对每个特征分别调整上下界，但存在权衡：小增量计算成本高，大增量则覆盖更少实例。为此，我们提出两种新方法。Onestep 在每特征每边界上一次性生成解释，消除迭代开销；Twostep 采用渐进策略，提升覆盖度。实验表明，相较于 Onestep 及先前方法，Twostep 在多个数据集上平均解释覆盖率提升达72.60%。
原文摘要 · Abstract (English)
Machine learning models support decision-making, yet the reasons behind their predictions are opaque. Clear and reliable explanations help users make informed decisions and avoid blindly trusting model outputs. However, many existing explanation methods fail to guarantee correctness. Logic-based approaches ensure correctness but often offer overly constrained explanations, limiting coverage. Recent work addresses this by incrementally expanding explanations while maintaining correctness. This process is performed separately for each feature, adjusting both its upper and lower bounds. However, this approach faces a trade-off: smaller increments incur high computational costs, whereas larger ones may lead to explanations covering fewer instances. To overcome this, we propose two novel methods. Onestep builds upon this prior work, generating explanations in a single step for each feature and each bound, eliminating the overhead of an iterative process. \textit{Twostep} takes a gradual approach, improving coverage. Experimental results show that Twostep significantly increases explanation coverage (by up to 72.60\% on average across datasets) compared to Onestep and, consequently, to prior work.
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