Knowledge Commons of Institute of Automation,CAS
Improving the Data Quality for Credit Card Fraud Detection | |
Rongrong Jing1,2; Hu Tian1,2; Yidi Li2; Xingwei Zhang1,2; Xiaolong Zheng1,2; Zhu Zhang1,3; Daniel Dajun Zeng1,2,3 | |
2022-11 | |
会议名称 | 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) |
会议日期 | 2022-11 |
会议地点 | Arlington, VA, USA |
摘要 | Label imbalance and data missing are two major challenges in the problem of credit card fraud detection. However, existing matrix completion algorithms are generally difficult and cannot be easily applied to real-world credit card fraud detection since the scale of the normally used dataset is oversized. In this paper, we develop a spectral regularization algorithm to complete the large-scale sparse matrices, and further utilize an over-sampling algorithm to tackle the problem of the imbalance between positive and negative samples. Experimental results on a real-world dataset demonstrate that our model can outperform the state-of-the-art baseline methods. The proposed method could also be extended to other large-scale scenarios where data is missing or labels are imbalanced. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48799 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Xiaolong Zheng |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Shenzhen Artificial Intelligence and Data Science Institude (Longhua) |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Rongrong Jing,Hu Tian,Yidi Li,et al. Improving the Data Quality for Credit Card Fraud Detection[C],2022. |
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