Summary of Globally Interpretable Classifiers Via Boolean Formulas with Dynamic Propositions, by Reijo Jaakkola et al.
Globally Interpretable Classifiers via Boolean Formulas with Dynamic Propositions
by Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach for extracting interpretable classifiers from tabular data is proposed, which enables the creation of Boolean formulas using extracted or computed propositions. The method employs Answer Set Programming and is compared to state-of-the-art algorithms XGBoost and random forests on seven datasets. While achieving similar accuracy levels, the new method’s classifiers are significantly shorter and human-readable, unlike the black-box nature of reference methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have developed a way to make AI models more understandable by creating short Boolean formulas from tabular data. This approach uses Answer Set Programming to extract or compute propositions that can be used to create simple formulas. The method is tested on seven datasets and compared to popular algorithms like XGBoost and random forests. The results show that the new method produces classifiers that are just as accurate but much easier for humans to understand. |
Keywords
» Artificial intelligence » Xgboost
