A machine learning based credit card fraud detection using the GA algorithm for feature selection
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Authors
Ileberi, Emmanuel
Sun, Yanxia
Wang, Zenghui
Issue Date
2022-02-25
Type
Article
Language
Keywords
Machine learning , Genetic algorithm , Fraud detection , Cybersecurity
Alternative Title
Abstract
The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.
Description
Citation
Ileberi, E., Sun, Y. & Wang, Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data 9(1):24 (2022)