Summary of Explaining Predictions by Characteristic Rules, By Amr Alkhatib et al.
Explaining Predictions by Characteristic Rules
by Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel explanation technique, called CEGA (Characteristic Explanatory General Association rules), is proposed for improving interpretability of predictions. CEGA aggregates multiple explanations generated by standard local techniques into characteristic rules using association rule mining. The approach compares favorably to state-of-the-art methods, Anchors and GLocalX, in terms of fidelity and complexity. Results show that CEGA outperforms GLocalX in fidelity while competing with discriminative rules. The technique’s effectiveness is also demonstrated when combined with SHAP or Anchors as local explanation techniques. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to explain predictions by combining multiple explanations into characteristic rules. This helps make predictions more understandable and useful. Three methods are compared, and the new method does better than the others in some ways. The results show that this technique is good at making predictions accurate while also being easy to understand. |