## 5 Experimental Results
### Evaluation Results
In other words, they cause the algorithm to fall into the trap of local optima and premature convergence. Moreover, classifiers (i.e., KNN) are sensitive to outliers since they distort the model's understanding of the points' distribution, leading to biased predictions [26; 137; 138; 139; 140]. Also, these outliers indicate that the classifiers used may suffer from overfitting or underfitting. In Figures 6 and 7, outliers are represented with red plus signs, where any red plus sign under or above the bounds (represented with short black horizontal lines) is considered an outlier. MBHO with Spearman's correlation function suffered from outliers when using the Colon (one point), Leukemia (four points), Leukemia-3c (one point), Parkinson's (two points), Sobar (three points), Sonar (two points), SpectTF (three points), and the Sobar (three points). Figure 5: The convergence trends of MBHO, BCS [37], MBHO with Spearman’s coefficient, and BCS with Spearman’s coefficient were compared across six datasets. The \(X\)-axis represents the number of iterations, while the \(Y\)-axis indicates the average fitness value. The graph demonstrates rapid convergence towards a solution set during the initial iterations. MBHO stands out among other algorithms due to its focus on maximizing the fitness score. Furthermore, with each iteration, the performance of MBHO with Spearman’s improves, leading to the discovery of solutions with higher values by integrating the best solutions from previous iterations. The convergence curves of MBHO provide evidence that it surpasses BCS [37] in achieving superior solutions within the search space. points), Urban (four points), and WDBC (two points) datasets. The opposite occurred when using the Darwin, Divorce, MLL, SRBCT, and WPBC datasets. This low number of outliers shows overall stability in the datasets. Figure 6: The box plot of MBHO BCS [37], MBHO with [PERSON]’s coefficient, and BCS with Spearman’s coefficient across the first eight datasets. The \(X\)-axis represents all the algorithms involved in the comparisons, while the \(Y\)-axis indicates the average fitness value. The plot shows that, with six datasets, MBHO with Spearman’s coefficient had the highest fitness score. However, in some datasets, MBHO with Spearman’s coefficient also achieved large variability. This variability signifies the degree of spread in the fitness scores for each method. The outliers in the fitness function scores are shown with red-colored points. **Analyzing MBHO--correlation (Spearman's) feature selection algorithm with ROC curves:** ROC curves are widely used in evaluating the performance of binary classification problems. They provide a comprehensive visualization of a classifier's ability to discriminate between positive and negative classes across various decision thresholds. ROC curves plot the true positive rate (sensitivity) against the false positive rate (1-specificity), allowing us to assess the trade-off between sensitivity and specificity. ROC curves also provide a single metric, the area under the curve (AUC), which summarizes the classifier's performance across all possible thresholds, making it a valuable tool for representing the performance of FS algorithms. Figure 8 represents the ROC curves of MBHO--correlation ([PERSON]'s). The model achieved good (accurate) performance for the Darwin, WDBC, and Divorce datasets. Moreover, it achieved fair (acceptable) performance for the Colon, Parkinson's, and Sonar datasets. On the other hand, it achieved poor performance (low accuracy) and was no better than random guessing for the SpectTF and WPBC, datasets respectively. Figure 7: The box plot compares MBHO, BCS [37], MBHO with Spearman’s coefficient, and BCS with Spearman’s coefficient across the remaining six different datasets. The \(X\)-axis displays the algorithms being compared, while the \(Y\)-axis represents the average fitness value. The plot reveals that, across six datasets, MBHO with [PERSON]’s coefficient exhibited the highest fitness score.