## 5 Experimental Results
### Evaluation Results
Under the NB classifier, MBHO with Spearman's correlation function was ranked first, providing the highest mean and sum of ranks, and the null hypothesis was rejected. Overall, MBHO with Spearman's correlation function required more time to generate the final subset of features compared to the other methods since it is an approximation approach. These findings suggest that MBHO with Spearman's correlation function is a promising approach for FS tasks in different applications (captured using different datasets), offering competitive performance across diverse datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Name**} & \multicolumn{8}{c}{**MBHO—Correlation**} & \multicolumn{8}{c}{**BCS—Correlation [37]**} \\ \hline  & **Acc** & **F1** & **Precision Recall** & **AUC** & **Time** & **Features** & **Acc** & **F1** & **Precision Recall** & **AUC** & **Time** & **Features** \\ \hline \multicolumn{10}{c}{**KNN classifier with \(K=3\)**} \\ \hline Colon & **0.86** & 0.83 & 0.86 & 0.86 &  
19:16:256, whereas MBHO--correlation ([PERSON]'s) took a significant time of 2:48:04:767. For the Darwin dataset, MBHO--correlation ([PERSON]'s) was executed in 4:43:962, whereas MBHO--correlation ([PERSON]'s) took more time at 33:58:890. In general, the FS process using MBHO--correlation ([PERSON]'s) took less time than MBHO--correlation ([PERSON]'s) for six out of eight datasets. Here, we report the cases where using [PERSON]'s correlation function achieved poorer performance than using [PERSON]'s correlation function. Under the KNN classifier, MBHO--correlation ([PERSON]'s) provided better performance than MBHO--correlation ([PERSON]'s) with the Colon, Darwin, Sonar, Divorce, and WPBC datasets. Under the LR classi  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Name** & \multicolumn{4}{c}{**MBHO—Correlation ([PERSON]’s)**} & \multicolumn{4}{c}{**MBHO—Correlation ([PERSON]’s)**} \\ \hline  & **Acc** & **F1** & **Precision** & **Recall** & **AUC** & **Time** & **Features** & **Acc** & **F1** & **Precision** & **Recall** & **AUC** & **Time** & **Features** \\ \hline \multicolumn{11}{c}{**KNN classifier with \(K=3\)**} \\ \hline Colon & **0.86** & 0.83 & 0.86  
fier, MBHO--correlation ([PERSON]'s) provided better performance than MBHO--correlation ([PERSON]'s) in the WPBC dataset, and it showed poorer performance for the remaining datasets. Under XGBoost, MBHO--correlation ([PERSON]'s) provided better performance than MBHO--correlation ([PERSON]'s) with the Colon, WDBC, and Sonar datasets, yet it showed poorer performance with the Divorce and SpectTF datasets. Under NB, MBHO--correlation ([PERSON]'s) provided better performance than MBHO--correlation ([PERSON]'s) with the WPBC dataset, and it showed poorer performance with the Darwin, Sonar, WDBC, and Parkinson's datasets. **Evaluation of convergence speed of proposed approach:** In this study, we analyzed the behavior of the convergence curves generated via the suggested algorithms. These algorithms are our method variants (MBHO--no correlation and MBHO--correlation), along with the BCS variants. The experiments were conducted based on the average fitness score of each algorithm over a series of iterations (ranging from 1 to 25). Figures 4 and 5 illustrate the convergence curve using the datasets provided, where the \(X\)-axis denotes the iteration count, and the \(Y\)-axis signifies the \(Acc\) value for each algorithm. According to Figures 4 and 5, the proposed MBHO model achieved faster convergence than the other algorithms due to its emphasis on maximizing the fitness score. Furthermore, the performance of the proposed algorithm was enhanced (as shown in the figures by tending to find solutions with higher values) at each iteration.