Summary of Abductive Explanations Of Classifiers Under Constraints: Complexity and Properties, by Martin Cooper and Leila Amgoud
Abductive explanations of classifiers under constraints: Complexity and properties
by Martin Cooper, Leila Amgoud
First submitted to arxiv on: 18 Sep 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 In this paper, researchers tackle the issue of abductive explanations (AXp’s) for classifier decisions when feature relationships are present. Existing definitions may lead to redundant or superfluous explanations when ignoring constraints between features. The authors propose three new types of AXp’s that account for these constraints and can be generated from the entire feature space or a sample dataset, based on the concept of coverage. They analyze the complexity of finding each type and investigate their formal properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve abductive explanations when dealing with dependent features. By introducing new types of AXp’s that consider constraints, researchers aim to eliminate redundant explanations. The study provides a range of options for generating AXp’s from the entire feature space or a sample dataset. Understanding these new approaches can help create more accurate and efficient classifier decisions. |
