https://doi.org/10.1140/epjs/s11734-025-01703-y
Regular Article
Enhancing variational quantum classifier performance with meta-heuristic feature selection for credit card fraud detection
1
Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, 54050, Sakarya, Turkey
2
Department of Computer Engineering, Institute of Natural Sciences, Sakarya University, 54050, Sakarya, Turkey
3
Department of Computer Engineering, Faculty of Computer and Information Sciences, Sakarya University, 54050, Sakarya, Turkey
4
Center for Quantum Computer Science, Faculty of Computing, University of Latvia, LV-1586, Riga, Latvia
Received:
31
March
2025
Accepted:
19
May
2025
Published online:
27
May
2025
A transformative hybrid approach is proposed, combining Quantum Machine Learning (QML) with both traditional and meta-heuristic feature selection algorithms to overcome the complexities and limitations of conventional credit card fraud detection methods. In this study, advanced data balancing techniques such as SMOTE-ENN (Synthetic Minority Over-sampling Technique–Edited Nearest Neighbors) and Random Under Sampler (RUS) are employed on the imbalanced European Cardholder Dataset to address class imbalance and enhance model resilience. Core feature selection algorithms—both traditional methods like K-Best and meta-heuristic techniques including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Atom Search Optimization (ASO)—are systematically evaluated, each paired with the Variational Quantum Classifier (VQC) for classification. Remarkably, the PSO+VQC combination achieves an accuracy rate of 94.54%, underscoring the efficacy of integrating meta-heuristic algorithms with VQC to manage complex, high-dimensional data in fraud detection. These findings highlight QML and meta-heuristic algorithms’ potential to surpass conventional methods, delivering superior accuracy and efficiency in critical, data-intensive applications such as financial fraud detection.
© The Author(s) 2025
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