将模糊逻辑融入深度符号回归，提升金融欺诈检测的可解释性与性能。
Integrating Fuzzy Logic into Deep Symbolic Regression
- 用模糊逻辑改进深度符号回归，处理欺诈数据中的不确定性
- Łukasiewicz 模型达到最高 F1 分数，表现最优
- 兼顾可解释性，适合需要透明决策的金融场景
信用卡欺诈检测是金融机构的重要关切，尤其在无接触支付技术普及的背景下。尽管深度学习模型具备高精度，但其缺乏可解释性，在金融场景中面临挑战。本文探索将模糊逻辑融入深度符号回归（DSR）以提升欺诈检测的性能与可解释性。研究对比了 Łukasiewicz、Gödel 与 Product 三种模糊蕴含算子在处理欺诈数据复杂性与不确定性时的效果。结果表明，Łukasiewicz 蕴含算子取得最高 F1 得分与整体准确率，而 Product 蕴含算子在性能与可解释性之间实现良好平衡。尽管由于数据转换带来的信息损失，性能低于当前最优（SOTA）模型，但本方法在模糊逻辑与 DSR 结合方面具有新颖性，提供了不同蕴含算子与方法的全面比较，为欺诈检测提供了新视角。
原文摘要 · Abstract (English)
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 Łukasiewicz, Gödel, and Product, in handling the complexity and uncertainty of fraud detection datasets. Our analysis 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. 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.
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