Logic-based Explanations for Linear Support Vector Classifiers with
  Reject Option
Francisco Mateus Rocha Filho Thiago Alves Rocha Reginaldo Pereira Fernandes Ribeiro Ajalmar Rêgo da Rocha Neto
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.