# Logic-based Explanations for Linear Support Vector Classifiers with Reject Option

arXiv: https://arxiv.org/abs/2403.16190 · HTML: https://mlsanity.com/paper/2403.16190/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018)
- [Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay](https://mlsanity.com/paper/2008.05803/) (2020)
- [On Guaranteed Optimal Robust Explanations for NLP Models](https://mlsanity.com/paper/2105.03640/) (2021)
- [On Explaining Decision Trees](https://mlsanity.com/paper/2010.11034/) (2020)
- [On Validating, Repairing and Refining Heuristic ML Explanations](https://mlsanity.com/paper/1907.02509/) (2019)
- [A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations](https://mlsanity.com/paper/1705.01968/) (2017)

## Built on by (most cited first, 3 of 3)

- [Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization](https://mlsanity.com/paper/2603.01870/) (2026)
- [Bound Propagation meets Constraint Simplification: Improving Logic-based XAI for Neural Networks](https://mlsanity.com/paper/2603.01923/) (2026)
- [Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option](https://mlsanity.com/paper/2603.14096/) (2026)

## Abstract (arXiv)

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.