## 5 Results and Discussion of Findings
### Performance Evaluation of State-of-the-Art Classifiers on Methods  
The classification accuracy analysis applied for the comparative analysis discussed in the previous subsection uses the KNN method. This subsection presents our investigation regarding whether using a different classifier from the list of existing state-of-the-art classifiers would improve the classification accuracy of the optimizers. Table 6 lists the comparative analysis of the influence of different classifiers is presented using the M-of-n dataset as a sample solution. The KNN, random forest (RF), MLP, decision tree (DTree), SVM, and Gaussian naive Bayes (GNB) classifiers were compared using the accuracy, precision, recall, F1-score and area under curve (AUC) metrics. The classification accuracy for KNN, RF, MLP, DTree, SVM, and GNB classifiers for BEOSA and BIEOSA were 0.815, 0.835, 0.84, 0.795, 0.845, and 0.83, 0.935, 0.665, 0.67, 0.67, 0.67, and 0.67 respectively. Results showed that the SVM and KNN worked well for the BEOSA and BIEOSA by obtaining classification accuracy of 0.845 and 0.935 for the SVM and KNN respectively. The most competing method, the BPSO algorithm, showed that the MLP and SVM classifiers are more suitable for obtaining better classification accuracy when compared with other classifiers. For precision-recall, F1-score and AUC, the values of 0.875, 1, 0.933333, and 0.993132 were obtained for the BEOSA, while the values of 1, 0.866667, 0.928571, and 1 were obtained for BIEOSA using the SVM and KNN classifiers respectively. This result confirms that when a classifier produces a good classification result on a binary optimizer, that classifier has a tendency to improve the results of the precision, recall, F1-score and area under curve (AUC) metrics as well. To provide a broader view of the performance of the KNN, RF, MLP, DT, SVM and GNB classifiers with population sizes of 25, 50, 75 and 100, we plotted graphs to show how the BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA algorithms performed. Figure 8 shows the results of the comparisons carried out using the CongressEW dataset as a sample. The BWOA algorithm performed well with SVM, BPSO performed well with KNN, BFSO performed well with MLP, BGWO performed well with RF, BDMO performed well with GNB, BSNDO performed well with GNB, SVM, MLP and RF, BEOSA performed well with KNN and BIEOSA performed well with SVM. These performance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline \multirow{8}{*}{BWOA} & KNN & 0.665 & 0.7 & 0.785714 & 0.733333 & 0.770667 \\  & RF & 0.71 & 0.714286 & 0.666667 & 0.666667 & 0.805333 \\  & MLP  
differences are a strong indication that research on the use of a binary optimizer to solve feature selection must not be limited to the performance of the optimizer alone, but rather, that efforts must be made to select a fitting classifier as well. Interestingly, we found that KNN and SVM, which are known to work well with most classification tasks, showed good performance with the proposed BEOSA and BIBOSA methods. The experimental results for the five classifiers using the CongressEW dataset with the BWOA algorithm showed that the classification accuracies of GNB and KNN were around 0.90 for a population size of 25, rising to a peak at 0.94 and 0.93 with a population size of 75. RF and SVM yielded the same value, i.e., 0.93, with a population size of 25 but peaked at around 0.96 with a population size of 75. In the middle of the curves is MLP curve, which has its peak classification value at 0.94 with a population size of 75; its lowest reported value was 0.84, with a population size of 50. With BFSO and BGWO, KNN, RF, MLP, DT, SVM and GNB achieved classification accuracies of 0.86, 0.92, 0.94, 0.86, 0.89, and 0.88 with a population size 50, and 0.935, 0.968, 0.949, 0.956, 0.962 and 0.953 with a population size of 50. In contrast, KNN achieved the best performance with a population size of 75.