Integrating Fuzzy Logic into Deep Symbolic Regression

TL;DR

This paper enhances deep symbolic regression for credit card fraud detection by integrating fuzzy logic, improving explainability and performance balance through various fuzzy implications.

Contribution

It introduces the integration of fuzzy logic into Deep Symbolic Regression for fraud detection, comparing different implications for improved explainability and performance.

Findings

{} Lukasiewicz implication yields highest accuracy.

Product implication balances performance and explainability.

Performance is lower than SOTA due to data transformation.

Abstract

Credit card fraud detection is a critical concern for financial institutions, intensified by the rise of contactless payment technologies. While deep learning models offer high accuracy, their lack of explainability poses significant challenges in financial settings. This paper explores the integration of fuzzy logic into Deep Symbolic Regression (DSR) to enhance both performance and explainability in fraud detection. We investigate the effectiveness of different fuzzy logic implications, specifically {\L}ukasiewicz, G\"odel, and Product, in handling the complexity and uncertainty of fraud detection datasets. Our analysis suggest that the {\L}ukasiewicz implication achieves the highest F1-score and overall accuracy, while the Product implication offers a favorable balance between performance and explainability. Despite having a performance lower than state-of-the-art (SOTA) models due to information loss in data transformation, our approach provides novelty and insights into into integrating fuzzy logic into DSR for fraud detection, providing a comprehensive comparison between different implications and methods.