## 6 Results
### Feature Reduction Analysis  
Figure 15 illustrates the average number of selected features across the three datasets. Figure 14: The average performance over 30 runs on Campaign dataset. Figure 15: The average number of selected features over 30 runs. * Taiwan Bankruptcy Dataset: With 96 features, FbQGA (31.52 features) demonstrated the most efficient reduction, followed closely by GbQGA and IbQGA, which also reduced dimensionality effectively. GA, in contrast, selected 41.10 features, leaving more irrelevant features and highlighting its limitations in reducing dimensions for datasets with relatively fewer features. * Musk Dataset: Among 167 features, FbQGA (22.13 features) again achieved the most substantial reduction, outperforming the other quantum-inspired variants and significantly outpacing GA (61.35 features). This dataset's moderate dimensionality underscores FbQGA's adaptability, where dynamic adjustments to feature importance contribute to efficient reduction. * Parkinson's Disease Dataset: With 754 features, this dataset posed a significant challenge. FbQGA (236.91 features) emerged as the most effective algorithm, selecting the fewest features and maintaining consistency. Other quantum-inspired algorithms, including GbQGA and IbQGA, performed well but selected slightly more features than FbQGA. GA (367.20 features) struggled, retaining nearly half of the original features. * Backdoor dataset, FbQGA selects the smallest number of features (13), significantly reducing dimensionality compared to GA (31) and even outperforming other quantum genetic algorithms such as IbQGA (17), GbQGA (19), and FvQGA (24). This suggests that FbQGA is the most efficient in feature selection, minimizing redundancy while maintaining useful information. Given its lower recall in the classification results, this aggressive reduction may contribute to missing some relevant fraud indicators, indicating a trade-off between feature selection and model sensitivity. * Campaign dataset, FbQGA again selects the fewest features (11), demonstrating its strong feature reduction capability compared to GA (18), FvQGA (16), GbQGA (15), and IbQGA (15). This suggests that FbQGA efficiently filters out irrelevant or redundant features while preserving those most critical for classification. Since FbQGA also achieved the highest accuracy in this dataset, the reduced feature set appears to be well-optimized, helping to enhance model performance without sacrificing predictive power. Overall, FbQGA consistently excelled in feature selection, demonstrating robust adaptability across datasets of varying complexity. Quantum-inspired methods, such as FbQGA, GbQGA, and IbQGA, significantly outperformed the traditional GA in reducing dimensionality, particularly for high-dimensional datasets. The processing time results, shown in Figure 16, further distinguish the performance of the algorithms. * Taiwan Bankruptcy Dataset: GA had the highest processing time (50.93 s), while FvQGA (31.92 s) and FbQGA (33.15 s) were the fastest. The quantum-inspired algorithms demonstrated efficiency, with FbQGA striking a balance between speed and feature selection effectiveness. * Musk Dataset: GA required the longest processing time (347.33 s), reflecting its inefficiency in handling moderate dimensionality. FvQGA (208.46 s) was the fastest but lacked the feature selection precision of FbQGA (215.46 s), which offered a superior trade-off between processing time and dimensionality reduction. * Parkinson's Disease Dataset: For this highly complex dataset, GA (143.88 s) was the slowest, while FbQGA (80.12 s) and GbQGA (80.01 s) were the most efficient. Despite their similar times, FbQGA demonstrated better feature selection, making it the optimal choice for extreme dimensionality. Backdoor dataset, the PbQGA algorithm has an average running time of 212 s, making it one of the more efficient methods compared to GA (564 s) and FuQGA (413 s).