Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization
This addresses the need for reliable and broad explanations in machine learning decision-making, though it is incremental as it builds on existing logic-based approaches.
The paper tackles the problem of generating correct but overly constrained logic-based explanations for machine learning classifiers by proposing two new methods, Onestep and Twostep, with Twostep increasing explanation coverage by up to 72.60% on average compared to prior work.
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.