## 6 Results
### Performance Analysis  
Figure 9 outlines the convergence plot for FbQGA versus the four other algorithms on the given datasets over 100 generations. The convergence plot of each method is the average of 30 runs. The Taiwan bankruptcy dataset (Figure 10) represents a moderately high-dimensional problem with a low anomaly rate. FbQGA emerged as the most effective algorithm, achieving the highest F1-score (\(58.86\pm 1.17\%\)), Recall (\(61.27\pm 5.06\%\)), and Accuracy (\(89.61\%\)). Its dynamic feature prioritization mechanism proved particularly effective, allowing it to handle the moderate dimensionality and capture the minority class with high precision and consistency. The low standard deviation across metrics further illustrates FbQGA's reliability. While GbQGA (\(58.93\pm 2.13\%\)) and IbQGA (\(57.93\pm 1.33\%\)) also performed reasonably well in terms of F1-score, they lacked the adaptability needed to match FbQGA's balanced performance. The classic GA struggled significantly, with the lowest F1-score (\(52.42\pm 2.26\%\)) and Accuracy (\(84.89\%\)), highlighting its inability to manage the dimensionality effectively. FvQGA showed some improvement over GA, but its fixed rotation angle limited its capability to optimize feature selection dynamically. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Evaluation Metric** & **IbQGA** & **IbQGA std** & **FbQGA** & **FbQGA std** & **SSD** \\ \hline \multirow{8}{*}{Parkinson’s Disease} & F1 & 0.6781 & 3.41\% & **0.6822** & 2.72\% & **yes** \\ \cline{2-6}  & Precision & 0.6713 & 2.89\% & **0.6872** & 2.34\% & **yes** \\ \cline{2-6}  & Recall & 0.6831 & 2.89\% & **0.7031** & 2.61\% & **yes** \\ \cline{2-6}  & Accuracy & 0.745 & 2.67\% & **0.7731** & 2.74\% & **yes** \\ \cline{2-6}  & Avg. No. of Selected Features & 243.26 & 29.45\% & **236.91** & 25.24\% & **yes** \\ \cline{2-6}  & Running Time (s) & **78.5213** & 16.12\% & 80.1122 & 16.05\% & no \\ \hline \multirow{8}{*}{Backdoor} & F1 & 0.7826 & 4.49\% & **0.8422** & 1.94\% & **yes** \\ \cline{2-6}  & Precision & 0.8711 & 2.10\% & **0.9205** & 5.10\% & **yes** \\ \cline{2-6}  & Recall & 0.7105 & 4.50\% & **0.7762** & 2.08\% & **yes** \\ \cline{2-6}  & Accuracy & 0.9247 & 2.20\% & **0.9547** & 2.20\% & **yes** \\ \cline{2-6}  & Avg. No. of Selected Features & 17.30 & 2.49\% & **12.50** & 3.78\% & **yes** \\ \cline{2-6}  & Running Time (s) & 231.89 & 27.18\% & **212.35** & 23.96\% & no \\ \hline \multirow{8}{*}{Campaign} & F1 & 0.6914 & 1.78\% & **0.6934** & 1.96\% & no \\ \cline{2-6}  & Precision & **0.6880** & 1.77\% & 0.6826 & 1.63\% & no \\ \cline{2-6}  & Recall & 0.6948 & 2.14\% & **0.7046** & 3.18\% & no \\ \cline{2-6}  & Accuracy & 0.8709 & 1.29\% & **0.8733** & 1.20\% & no \\ \cline{2-6}  & Avg. No. of Selected Features & 15.40 & 3.13\% & **10.70** & 4.32\% & **yes** \\ \cline{2-6}  & Running Time (s) & **92.48** & 14.24\% & 92.83 & 16.06\% & no \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(Cont.\)The Musk dataset (Figure 11) poses significant challenges for traditional algorithms like GA, which achieved an F1-score of \(60.96\pm 15.11\%\), highlighting its instability and inability to handle high dimensionality effectively. FbQGA, on the other hand, excelled with the highest F1-score (\(69.65\pm 5.48\%\)), Recall (\(74.5\pm 6.13\%\)), and Accuracy (\(71.2\pm 2.74\%\)). Its feature-based rotation adjustment mechanism proved instrumental in prioritizing significant features. GbQGA (\(67.56\pm 4.48\%\)) and IbQGA (\(67.62\pm 4.48\%\)) have comparable F1-scores, leveraging their generation- and individual-based rotation angle mechanisms to adapt to the dataset's high dimensionality. However, their lack of feature-specific adjustments left  
Figure 9: The Convergence curve of FbQGA versus other methods. proved insufficient for capturing meaningful patterns in the data, while GA's non-adaptive feature selection mechanism failed to optimize performance.