Support Vector Classifier (SVC) is a well-known Machine Learning (ML) model for linear classification problems. It can be used in conjunction with a reject option strategy to reject instances that are hard to correctly classify and delegate them to a specialist. This further increases the confidence of the model. Given this, obtaining an explanation of the cause of rejection is important to not blindly trust the obtained results. While most of the related work has developed means to give such explanations for machine learning models, to the best of our knowledge none have done so for when reject option is present. We propose a logic-based approach with formal guarantees on the correctness and minimality of explanations for linear SVCs with reject option. We evaluate our approach by comparing it to Anchors, which is a heuristic algorithm for generating explanations. Obtained results show that our proposed method gives shorter explanations with reduced time cost.

翻译：支持向量机分类器（SVC）是线性分类问题中一种著名的机器学习（ML）模型。它可与拒绝选项策略结合使用，将难以正确分类的实例拒绝并交由专家处理，从而进一步提升模型的置信度。基于此，获取拒绝原因的解释对于避免盲目信任所得结果至关重要。尽管现有相关研究已开发出为机器学习模型提供此类解释的方法，但据我们所知，尚无研究针对存在拒绝选项的场景进行解释。我们提出一种基于逻辑的方法，该方法在包含拒绝选项的线性SVC解释的正确性与最小性方面具有形式化保证。通过与启发式解释生成算法Anchors进行对比实验，结果表明我们的方法能以更低的时间成本生成更简洁的解释。