Improving the Data Quality for Credit Card Fraud Detection
文献类型:会议论文
作者 | Rongrong Jing2,3; Hu Tian2,3; Yidi Li2; Xingwei Zhang2,3; Xiaolong Zheng2,3; Zhu Zhang1,3; Daniel Dajun Zeng1,2,3 |
出版日期 | 2022-11 |
会议日期 | 2022-11 |
会议地点 | Arlington, VA, USA |
英文摘要 | 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. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/48799] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
通讯作者 | Xiaolong Zheng |
作者单位 | 1.Shenzhen Artificial Intelligence and Data Science Institude (Longhua) 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences |
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]. 见:. Arlington, VA, USA. 2022-11. |
入库方式: OAI收割
来源:自动化研究所
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