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Khine, Aye Aye
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Computer Science & IT Electrical & Electronics Engineering

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Credit card fraud data analysis using proposed sampling algorithm and deep ensemble learning Khine, Aye Aye; Myint, Zin Thu Thu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp311-320

Abstract

Credit card fraud detection is challenging due to the severe imbalance between legitimate and fraudulent transactions, which hinders accurate fraud identification. To address this, we propose a deep learning-based ensemble model integrated with a proposed sampling algorithm based on random oversampling. Unlike traditional methods, the proposed sampling algorithm addresses the oversight of parameter selection and manages class imbalance without eliminating any legitimate samples. The ensemble framework combines the strengths of convolutional neural networks (CNN) for spatial feature extraction, long short-term memory (LSTM) networks for capturing sequential patterns, and multilayer perceptrons (MLP) for efficient classification. Three ensemble strategies—Weighted average, unweighted average, and unweighted majority voting—are employed to aggregate predictions. Experimental results show that all ensemble methods achieve perfect scores (1.00) in precision, recall, and F1-score for both fraud and non-fraud classes. This study demonstrates the effectiveness of ensemble model with optimized sampling approach for robust and accurate fraud detection.
Co-Authors Myint, Zin Thu Thu