Summary of Preference-based Abstract Argumentation For Case-based Reasoning (with Appendix), by Adam Gould et al.
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
by Adam Gould, Guilherme Paulino-Passos, Seema Dadhania, Matthew Williams, Francesca Toni
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper introduces a novel approach called Preference-Based Abstract Argumentation for Case-Based Reasoning (AA-CBR-P), which allows users to define multiple comparison approaches and specify their preference over these approaches. This enables the model to inherently follow user-defined preferences when making predictions, improving the efficacy and flexibility of interpretable classification models. The authors demonstrate the effectiveness of AA-CBR-P on a real-world medical dataset from a clinical trial evaluating different assessment methods for patients with primary brain tumours. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for people to make better decisions by creating a new way to use computers. It’s called Preference-Based Abstract Argumentation for Case-Based Reasoning, or AA-CBR-P for short. This tool lets users choose how they want to compare things and rank their preferences. The computer then uses these rules to make predictions that are more accurate and useful. The authors tested this method on a real-life medical problem and found it outperformed other methods. |
Keywords
» Artificial intelligence » Classification
