混合功能选择框架用于使用机器学习模型增强信用卡欺诈检测.
Al Mahmud Siam1, Pankaj Bhowmik1, Md Palash Uddin1
1Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
PloS one
|July 16, 2025
概括
本研究引入了一种混合功能选择框架,以改善信用卡欺诈检测. 这种新的方法在不平衡的数据集上提高了机器学习模型的性能,为现实世界的应用提供了实用解决方案.
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
- 机器学习 机器学习
背景情况:
- 电子支付已经广泛普及,但越来越多的信用卡欺诈导致了巨大的财务损失.
- 由于高度不平衡的数据集,检测信用卡欺诈是很困难的,欺诈性交易很少发生.
- 现有的方法与固有的数据不平衡作斗争,需要改进的特征选择技术.
研究的目的:
- 提出一种新的混合功能选择框架,以加强基于机器学习的信用卡欺诈检测.
- 整合皮尔森相关性,信息获取 (IG) 和随机森林重要性 (RFI) 以优化特征选择.
- 在不同的数据集和各种机器学习算法上验证框架的有效性.
主要方法:
- 一个混合特征选择框架,结合了皮尔森相关性,信息获取 (IG) 和随机森林重要性 (RFI).
- 皮尔森相关性消除了冗余的特征,而IG和RFI评估了特征的相关性.
- 工会运作将选定的特征合并为全面和高效的选择,在PCA转换和现实世界数据集上进行测试.
主要成果:
- 拟议的混合特征选择框架在五个不同的数据集中显著优于基线方法.
- 使用机器学习算法实现了卓越的欺诈检测性能,例如随机森林,XGBoost和CatBoost.
- 该方法在增强欺诈检测能力方面表现出强度和适应性.
结论:
- 新的混合功能选择框架为检测信用卡欺诈提供了实用和有效的解决方案.
- 该方法解决了不平衡数据集的挑战,提高了机器学习模型的准确性.
- 该框架有可能作为实时决策支持系统,有利于金融行业.
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