Research Article
A Fraud Detection Approach Based on Combined Feature Weighting
@INPROCEEDINGS{10.1007/978-3-030-69992-5_6, author={Xiaoqian Liu and Chenfei Yu and Bin Xia and Haiyan Gu and Zhenli Wang}, title={A Fraud Detection Approach Based on Combined Feature Weighting}, proceedings={Cloud Computing. 10th EAI International Conference, CloudComp 2020, Qufu, China, December 11-12, 2020, Proceedings}, proceedings_a={CLOUDCOMP}, year={2021}, month={2}, keywords={Fraud detection Imbalanced dataset Fisher score Feature weighting}, doi={10.1007/978-3-030-69992-5_6} }- Xiaoqian Liu
Chenfei Yu
Bin Xia
Haiyan Gu
Zhenli Wang
Year: 2021
A Fraud Detection Approach Based on Combined Feature Weighting
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-69992-5_6
Abstract
Data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. However, due to the vulnerabilities of credit card transactions, criminals are able to commit fraud to infringe on the interests of the state and citizens. How to discover potential fraudsters while guaranteeing high efficiency becomes an extremely valuable problem to solve. In this work, we talk about the advantages and disadvantages of different models to detect credit card fraud. We first introduce the data preprocessing measures for handling imbalanced fraud detection dataset. Then we compare related models to implement fraudster recognition. We also propose a feature selection approach based on combined feature weights. Some future research interests are also envisioned.
