Summary of Integrating Fuzzy Logic Into Deep Symbolic Regression, by Wout Gerdes and Erman Acar
Integrating Fuzzy Logic into Deep Symbolic Regression
by Wout Gerdes, Erman Acar
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO); Symbolic Computation (cs.SC)
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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 proposes an innovative approach to credit card fraud detection by integrating fuzzy logic into Deep Symbolic Regression (DSR). The aim is to enhance both the performance and explainability of DSR models, which are critical concerns for financial institutions. The authors investigate the effectiveness of different fuzzy logic implications (Łukasiewicz, Gödel, and Product) in handling the complexity and uncertainty of fraud detection datasets. The results suggest that the Łukasiewicz implication achieves the highest F1-score and overall accuracy, while the Product implication offers a favorable balance between performance and explainability. Although the approach has lower performance than state-of-the-art models due to information loss in data transformation, it provides novel insights into integrating fuzzy logic into DSR for fraud detection. This study contributes to the development of more transparent and effective fraud detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to detect credit card fraud. Right now, deep learning models are very good at detecting fraud, but they don’t explain why they’re making certain decisions. This can be a problem for financial institutions that need to know how to prevent fraud in the future. The authors of this paper tried combining fuzzy logic with Deep Symbolic Regression (DSR) to make a model that’s both good at detecting fraud and explains its decisions. They tested different types of fuzzy logic and found that one type, called Łukasiewicz, worked best. Although their approach wasn’t as good as some other models, it offers new ideas for how to make fraud detection systems more transparent and effective. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Regression
