# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 5 Results and Discussion
\begin{table}
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**Model** & **GAN** & **AE-GAN** & **AE** & **VAE** & **SMOTE** & **ADASYN** \\ \hline LR & 0.451 & 0.453 & 0.452 & 0.412 & 0.428 & 0.348 \\ DT & 0.574 & 0.569 & 0.572 & 0.514 & 0.564 & 0.586 \\ RF & 0.688 & 0.697 & 0.694 & 0.664 & 0.685 & 0.671 \\ GB & 0.667 & 0.673 & 0. (\(p\)-value = 0.0061), AE-GAN (\(p\)-value = 0.0104), and AE (\(p\)-value = 0.0134). These findings highlight the superior performance of VAE and ADASYN in enhancing BFDS metrics. Conversely, AE-GAN and AE do not exhibit significant differences from each other (\(p\)-value = 0.0572), indicating their comparable performance. Moreover, SMOTE does not show statistically significant improvements over most techniques, as reflected in its relatively high \(p\)-values, particularly against VAE (\(p\)-value = 0.0409) and ADASYN (\(p\)-value = 0.0803). Overall, these results reinforce the effectiveness of VAE and ADASYN in improving BFDS performance compared to traditional oversampling techniques. In contrast, methods like AE-GAN, AE, and SMOTE exhibit more comparable performance levels, with fewer statistically significant differences. The boxplot in Figure 13 illustrates the distribution of Balanced F-Measure (BFDS) scores across various oversampling techniques used with different classifiers. AE-GAN stands out with the highest median BFDS scores, indicating its superior ability to enhance classifier performance consistently. The narrow interquartile range (IQR) and minimal outliers for AE-GAN suggest stable and reliable results across different models. In contrast, traditional oversampling techniques like SMOTE and ADASYN exhibit moderate median BFDS scores with greater variability, showing that while effective, they do not consistently match the performance enhancement provided by AE-GAN. Other techniques, such as GAN, AE, and VAE, generally display lower median BFDS scores and wider IQRs, reflecting less effective performance improvements. Notably, classifiers like NB and RNN consistently show the lowest BFDS scores across all oversampling techniques, with high variability indicating persistent challenges in achieving balanced performance. Overall, AE-GAN emerges as the most effective technique for optimizing BFDS scores, offering a more reliable and enhanced performance across a range of classifiers compared to other methods. Figure 14 provides a visual comparison of Balanced F-Measure (BFDS) scores for various machine learning models across different oversampling techniques. AE-GAN stands out with the highest BFDS scores, indicating its effectiveness in enhancing classifier performance across most models. The bars representing AE-GAN are notably higher, reflecting its superior ability to balance sensitivity and precision. In contrast, traditional oversampling techniques such as SMOTE and ADASYN show moderate BFDS scores, with bars that are shorter than those for AE-GAN, suggesting less consistent performance improvement. Techniques like GAN, AE, and VAE also exhibit lower BFDS scores, as evidenced by their shorter bars, indicating that they provide less significant gains in balancing performance. The bars for NB and RNN are the shortest across all techniques, highlighting their lower BFDS scores and challenges in achieving balanced performance. Overall, the bar plot underscores AE-GAN as the most effective oversampling technique for optimizing BFDS scores, offering superior performance across various classifiers compared to other methods. Figure 15 shows the Balanced F-Measure (BFDS) scores for various machine learning models across different oversampling techniques, with the x-axis representing the models and the y-axis depicting the BFDS scores. Notably, the orange line, which represents the AE-GAN technique, stands out with consistently high BFDS scores across most models.