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: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/12/7254 |
| _version_ | 1797596268016435200 |
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| author | Ibomoiye Domor Mienye Yanxia Sun |
| author_facet | Ibomoiye Domor Mienye Yanxia Sun |
| author_sort | Ibomoiye Domor Mienye |
| collection | DOAJ |
| description | 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 real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature. |
| first_indexed | 2024-03-11T02:48:07Z |
| format | Article |
| id | doaj.art-d45d4a6dc8e6436f84175e788a7aabba |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:48:07Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d45d4a6dc8e6436f84175e788a7aabba2023-11-18T09:11:19ZengMDPI AGApplied Sciences2076-34172023-06-011312725410.3390/app13127254A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud DetectionIbomoiye Domor Mienye0Yanxia Sun1Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South AfricaDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South AfricaWith 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 real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature.https://www.mdpi.com/2076-3417/13/12/7254credit cardfeature selectionfraud detectiongenetic algorithmmachine learning |
| spellingShingle | Ibomoiye Domor Mienye Yanxia Sun A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection Applied Sciences credit card feature selection fraud detection genetic algorithm machine learning |
| title | A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection |
| title_full | A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection |
| title_fullStr | A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection |
| title_full_unstemmed | A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection |
| title_short | A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection |
| title_sort | machine learning method with hybrid feature selection for improved credit card fraud detection |
| topic | credit card feature selection fraud detection genetic algorithm machine learning |
| url | https://www.mdpi.com/2076-3417/13/12/7254 |
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