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Growing Science » International Journal of Data and Network Science » Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print)
Quarterly Publication
Volume 8 Issue 1 pp. 463-472 , 2024

Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks Pages 463-472 Right click to download the paper Download PDF

Authors: Enas Rawashdeh, Nancy Al-Ramahi, Hadeel Ahmad, Rawan Zaghloul

DOI: 10.5267/j.ijdns.2023.9.009

Keywords: Feature Selection, Fraud Detection, Machine Learning, Classification, Credit Card, Random weight network

Abstract: In the realm of financial security, the detection and prevention of credit card fraud has become paramount. With the ever-increasing reliance on digital transactions, the risk of fraudulent activities targeting credit card systems has grown significantly. To combat this, sophisticated techniques are required to swiftly identify and mitigate potential threats. Machine learning, a cornerstone of modern data analysis, has emerged as a powerful tool in this pursuit. By leveraging vast datasets and employing advanced algorithms, machine learning enables the automated scrutiny of transactions, distinguishing between legitimate and fraudulent activities with remarkable precision. This paper introduces an intelligent method for credit card fraud detection that relies on Competitive Swarm Optimization (CSO) and Random Weight Network (RWN). Additionally, the system includes an automated hybrid feature selection capability to identify the most pertinent features during the detection process. The experimental outcomes validate that this system can attain outstanding results in G-Mean, RUC, and Recall values.

How to cite this paper
Rawashdeh, E., Al-Ramahi, N., Ahmad, H & Zaghloul, R. (2024). Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks.International Journal of Data and Network Science, 8(1), 463-472.

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Journal: International Journal of Data and Network Science | Year: 2024 | Volume: 8 | Issue: 1 | Views: 1368 | Reviews: 0

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