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.

通过采用逻辑推理的方法，我们提出了一种在具有拒绝选项的线性支持向量分类器中，能够得到具有正确性和最小化特性的解释的形式化保证的途径，并将其与 Anchors 算法进行了比较，结果表明我们的方法能够以较短的时间得到较短的解释。