## 5 Results and Discussion of Findings
### Comparative Analysis of the Classification Accuracy of All Methods
The BWOA and BGWO algorithms showed average performances in the three datasets by obtaining their peak classification accuracy values of 0.79 and 0.80 at 50 and 75 population size with M-of-n, 0.845 and 0.835 both at 75 population size with Ionosphere, and 0.67 and 0.69 at 50 and 75 population size with Exactly. BEOSA obtain its best accuracy at 0.85 using 50 population size, 0.910 using 50 population size, and 0.74 using 100 population size for M-of-n, Ionosphere and Exactly datasets, respectively. The Exactly2 and HeartEW datasets showed that BSNDO results in the same classification accuracy for all population sizes at around 0.580 and 0.480, respectively. This is followed by BDMO, which obtained the best classification accuracies at 0.685 and 0.57 using 75 and 50 population sizes. The BWOA, BPSO and BIEOSA algorithms are seen to overlap in performances on the two datasets, with each reporting peak accuracy at 0.725, 0.710, and 0.750 using 50, 25 and 25 population sizes on the Exactly2 dataset. Similarly, BWOA, BPSO and BIEOSA showed their peak accuracies at 0.69 using 100, 100 and 25 population sizes. BPSO and BEOSA demonstrate a strong competitive performance by having their peak accuracy values at around 0.750 in Exactly2 and 0.80 in HeartEW, in both cases at 100 population size. Results obtained for CongressEW, Lymphography and BreastEW datasets showed that the BSNDO algorithm performances are almost similar for all population sizes at 0.63, 0.45, and 0.69, respectively. This is followed by the BDMO algorithm, which has its peak accuracies at 0.82, 0.63, and 0.89 using population sizes 50, 75, and 25 on the three datasets. The BIEOSA obtained its best classification accuracies at 0.90 using 100 population size,0.69 using population size, and 0.91 using 75 population size for CongressEW Lymphography and BreastEW, respectively. BWOA and BGWO competed in performance as seen on their curves in CongressEW Lymphography and BreastEW where BWOA had its best accuracies at 0.93, 0.77, and 0.93 using 75, 50 and 50 population sizes. Similarly, BPSO and BEOSA both peaked in performance by obtaining 0.95, between [0.8-0.9], and around 0.95, all using 75 population size in the three datasets. We observed the curves on the Colon and SpectEW datasets for all algorithms. In both cases, BDMO curves rank lowest at the bottom of the graphs having its peak performances as 0.75 and 0.735, using 25 and 100 population sizes. BSNDO shows close flat curves in both datasets, and peak performances averaged at 0.85 and 0.74 for Colon and SpectEW, respectively. In the SpectEW dataset, BWOA, BIEOSA and BGWO all reported their peak performances at around 0.80 and using 100, 50, and 50 population sizes, while the same algorithms had different curve patterns on Colon. For instance, BIEOSA peak accuracy is at 0.88 and 0.81 using 100 and 50 population size, BWOA peak accuracy is at 0.98 and close to 0.82 using 50 and 100 population sizes, and BGWO peak accuracy is at 0.98 and 0.81 using 75 and 50 population size. The summary of the results obtained for the classification accuracies on each algorithm with respect to all datasets is consistent with the performance reported for cost function evaluation. BPSO and BEOSA algorithms are seen to perform very well compared with other methods, but in most cases, the proposed BEOSA algorithm yields better performance than BPSO. These consistent performances of BEOSA with regard to fitness function evaluation, cost function evaluation, and classification accuracy for the selected feature sizes confirm the relevance of the algorithm in solving the feature selection problem. The performance superiority demonstrated by the BEOSA and BIEOSA methods in this comparative analysis for this subsection reinforced the argument that the proposed method is suitable for solving the feature selection problem. This finding is supported by the fact the minimal and optimal number of features selected by the BEOSA and BIEOSA methods were sufficient and determinant enough to yield the best classification accuracy.