Summary of Logic-based Explanations For Linear Support Vector Classifiers with Reject Option, by Francisco Mateus Rocha Filho et al.
Logic-based Explanations for Linear Support Vector Classifiers with Reject Option
by Francisco Mateus Rocha Filho, Thiago Alves Rocha, Reginaldo Pereira Fernandes Ribeiro, Ajalmar Rêgo da Rocha Neto
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel logic-based approach to provide explanations for linear Support Vector Classifier (SVC) models with reject options. This approach aims to identify the cause of rejection in instances deemed hard to classify and delegate them to specialists. The proposed method ensures correctness, minimality, and efficiency, making it suitable for large-scale applications. Unlike existing methods that focus on providing explanations without considering reject options, this work addresses this gap by developing a comprehensive framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about creating a new way to explain why some things are rejected when they’re hard to correctly classify using Support Vector Classifiers with a “reject” option. This helps make sure we don’t just blindly trust the results without knowing why certain things were rejected. Right now, most methods for explaining machine learning models only work when there’s no reject option. This research fills that gap by creating a logic-based approach to provide explanations that are correct, minimal, and efficient. |
Keywords
* Artificial intelligence * Machine learning
