Knowledge Commons of Institute of Automation,CAS
A GNN-based Few-shot learning model on the Credit Card Fraud detection | |
Rongrong Jing1,2; Hu Tian1,2; Gang Zhou1,2; Xingwei Zhang1,2; Xiaolong Zheng1,2; Daniel Dajun Zeng1,2 | |
2021-09 | |
会议名称 | 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) |
会议日期 | 2021-07-15_2021-08-15 |
会议地点 | Beijing, China |
摘要 | In the era of big data, large-scale data can be very effective in improving model performance. However, in the real world, high-quality data is usually difficult to acquire due to privacy or cost. Especially when it comes to credit card fraud, the fraud samples are quite rare. Detecting card fraud with few samples is a meaningful task. Graph neural network (GNN) is a good way to deal with few samples because an advantage of GNN is that information can be disseminated through connections between nodes. However, the data structure of credit cards cannot be applied by the GNN-based method directly. In this paper, we proposed a GNN-based few-shot learning method which can detect credit card fraud with few samples effectively. We constructed a learnable parametric adjacency matrix method relying on the similarity of features to pass messages and utilized the GCN layer to extract node features. We compared our method with classical machine learning algorithms and other graph neural networks on the real-world data set. Our experimental results show that our proposed model can perform better extremely with fewer training samples than baselines. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48815 |
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.University of Chinese Academy of Sciences |
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,Gang Zhou,et al. A GNN-based Few-shot learning model on the Credit Card Fraud detection[C],2021. |
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A_GNN-based_Few-shot(430KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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