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

页码：1136 / 1140

页数：5

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