相关实验视频
通过使用传统和深度学习模型加强信用卡欺诈检测,并减轻阶级不平衡
Tahani Albalawi1, Samia Dardouri1,2
1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.
Frontiers in artificial intelligence
|October 24, 2025
概括
这项研究通过比较机器学习模型来加强金融欺诈的检测. 随机森林模型显示出优越的整体性能,而深度学习则在识别欺诈交易方面表现出色.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 金融技术 金融技术
背景情况:
- 越来越复杂的欺诈活动对金融交易安全构成重大挑战.
- 强有力的欺诈检测对于减轻重大财务损失至关重要.
研究的目的:
- 评估和比较各种机器学习模型在检测金融欺诈方面的有效性.
- 解决阶级不平衡问题,提高欺诈检测系统的预测准确性.
主要方法:
- 对物流回归,决策树和随机森林模型进行比较分析.
- 开发一个深度学习模型,用于增强检测的焦点损失.
- 合成少数人过量采样技术 (SMOTE) 的应用,用于类不平衡和超参数调整.
主要成果:
- 随机森林模型实现了99.95%的准确性,0.8256的F1得分和0.9759的ROC-AUC.
- 深度学习模型展示了最高的精度,有效地最大限度地减少了假阳性.
- 在Kaggle信用卡和PaySim合成移动货币数据集中验证.
结论:
- 随机森林模型为欺诈检测提供了卓越的整体性能.
- 焦点丧失的深度学习显示出对精确识别欺诈性交易的承诺.
- 将数据预处理,重新抽样和模型优化结合起来,可以产生强大的欺诈检测能力.
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