A Comparison of Data Balancing Techniques for Credit Card Fraud Detection using Neural Network

被引：2
|
作者：
Uttam, Atul Kumar [1 ]
Sharma, Gaurav [1 ]
机构：
[1] GLA Univ, Dept Comp Engn & Applicat, Matthura, Uttar Pradesh, India
来源：
PROCEEDINGS OF THE 2021 FIFTH INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC 2021) | 2021年
关键词：
credit card; fraud detection; data balancing; SMOTE; SMOTETomek;
D O I：
10.1109/I-SMAC52330.2021.9640911
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This research study has employed an unequalled credit card dataset and evaluated several approaches of data balancing by using a basic neural network-based model with only one hidden layer of size 32. Even with a simplified design, the supervised machine-learning model has produced optimal results in contrast to sample-based approaches for the over-sampling of data balance. The proposed model based on random over-sampling performs better than other two model based on over-sampling approaches (SMOTE, SMOTETomek).
引用
收藏
页码：1136 / 1140
页数：5
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