软投票组合学习方法用于信用卡欺诈检测和检测信用卡欺诈
Mimusa Azim Mim1, Nazia Majadi1, Peal Mazumder1
1Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali-3814, Bangladesh.
Heliyon
|February 9, 2024
概括
一种新的软投票组合学习方法有效地检测不平衡数据集中的信用卡欺诈行为. 这种方法提高了准确性,并降低了金融机构的错误分类成本.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 金融技术 金融技术
背景情况:
- 信用卡欺诈是一个日益严重的全球问题,每年损失数十亿美元.
- 交易数据中的阶级不平衡阻碍了准确的欺诈检测.
- 现有的机器学习方法在不平衡的数据集上难以准确.
研究的目的:
- 提出一种新的软投票集体学习方法,用于检测信用卡欺诈.
- 为应对金融交易数据中阶级不平衡的挑战.
- 提高欺诈检测系统的准确性和降低错误分类成本.
主要方法:
- 开发了一种软投票集体学习模型,用于检测信用卡欺诈.
- 根据各种采样技术 (过量采样,不足采样,混合) 评估模型.
- 对不平衡数据进行采样方法和没有采样方法的组合分类器进行比较.
主要成果:
- 拟议的软投票整体方法显著优于个别分类器.
- 实现了高性能指标:精度为0.9870,回忆为0.9694,F1得分为0.8764,AUROC为0.9936.
- 在缓解阶级不平衡问题方面表现出有效性.
结论:
- 软投票组合学习是一种强大的方法,用于在不平衡数据上检测信用卡欺诈.
- 拟议的方法为金融机构提供了一种可靠的解决方案来打击欺诈行为.
- 这种方法通过提高欺诈识别准确度来提高支付系统的完整性.
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