A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence of redundant and irrelevant features in most...
Main Authors: | Ibomoiye Domor Mienye, Yanxia Sun |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-06-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/12/7254 |
Similar Items
-
A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection
by: Ibomoiye Domor Mienye, et al.
Published: (2023-01-01) -
A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection
by: Ibomoiye Domor Mienye, et al.
Published: (2024-10-01) -
Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
by: Ibomoiye Domor Mienye, et al.
Published: (2024-01-01) -
RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System
by: Bharat Kumar Padhi, et al.
Published: (2022-11-01) -
Application of the automated teller machine (ATM) card digit validation algorithm as a credit card fraud detection system
by: Khairul Muttaqin, et al.
Published: (2022-06-01)