Article: Parallelisation of machine learning models for credit card fraud detection Journal: International Journal of System of Systems Engineering (IJSSE) 2023 Vol.13 No.4 pp.386 - 406 Abstract: The use of credit cards for financial transactions is widely spreading in the current era. Credit card usage and fraud transactions are proportional to each other. This research analyses the impact of machine learning techniques such as base classifiers and ensemble models to detect fraud transactions. This is considered as a system of systems as there are a collection of many independent base and ensemble models that work together for prediction. Hyper-parameter tuning is an integral activity for enhancing model prediction. Two types of parameter tuning are performed: random and grid search. In addition, a comparative analysis is performed to evaluate the impact of cores. The Kaggle credit card fraud identification dataset is utilised for experimental analysis. While the analysis indicates that the training time of 5-classifiers is diminished, it has not been parallelised with 12-classifiers. The experimental analysis has an accuracy of 99.9% for 5-classifiers, including random and ensemble classifiers with hyperparameter tuning. The system's performance is also estimated using derived metrics such as precision, recall, f-score, and Matthews correlation coefficient. Inderscience Publishers - linking academia, business and industry through research

Title: Parallelisation of machine learning models for credit card fraud detection

Authors: J. Saira Banu; Sumaiya Thaseen Ikram; Vanitha Mohanraj; Kishore Sanapala; Polinpapilinho F. Katina

Addresses: School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India ' Department of ECE, Marri Laxman Reddy Institute of Technology and Management, Hyderabad, 500043, India ' Department of Informatics and Engineering Systems, University of South Carolina Upstate, Spartanburg, South Carolina, 29303, USA

Abstract: The use of credit cards for financial transactions is widely spreading in the current era. Credit card usage and fraud transactions are proportional to each other. This research analyses the impact of machine learning techniques such as base classifiers and ensemble models to detect fraud transactions. This is considered as a system of systems as there are a collection of many independent base and ensemble models that work together for prediction. Hyper-parameter tuning is an integral activity for enhancing model prediction. Two types of parameter tuning are performed: random and grid search. In addition, a comparative analysis is performed to evaluate the impact of cores. The Kaggle credit card fraud identification dataset is utilised for experimental analysis. While the analysis indicates that the training time of 5-classifiers is diminished, it has not been parallelised with 12-classifiers. The experimental analysis has an accuracy of 99.9% for 5-classifiers, including random and ensemble classifiers with hyperparameter tuning. The system's performance is also estimated using derived metrics such as precision, recall, f-score, and Matthews correlation coefficient.

Keywords: classification; cybersecurity; decision-making; embedded systems; machine learning; parallelisation.

DOI: 10.1504/IJSSE.2023.134436

International Journal of System of Systems Engineering, 2023 Vol.13 No.4, pp.386 - 406

Received: 31 Jul 2022
Accepted: 03 Nov 2022
Published online: 23 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Parallelisation of machine learning models for credit card fraud detection

Keep up-to-date