[2603.01870] Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization

Computer Science > Logic in Computer Science arXiv:2603.01870 (cs) [Submitted on 2 Mar 2026] Title:Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization Authors:Francisco Mateus Rocha Filho, Ajalmar Rêgo da Rocha Neto, Thiago Alves Rocha View a PDF of the paper titled Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization, by Francisco Mateus Rocha Filho and Ajalmar R\^ego da Rocha Neto and Thiago Alves Rocha View PDF HTML (experimental) Abstract: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...