## 5 Results and Discussion of Findings
### Comparative Analysis of the Classification Accuracy of All Methods  
The average number of selected features influences the classification accuracy, with a lower number of features being desirable so that classification operation is not bottlenecked. In this subsection, we evaluate the performance of BIEOSA and BEOSA and compare these approaches to related methods. Moreover, a comparative analysis is done in a manner that considers the influence of the population size on performance. Similar to the previous subsection, population sizes of 25, 50, 75, and 100 were compared for each binary optimizer algorithm. Table 5 shows the performance of the BreastEW, Exactly2, HeartEW and Ionosphere datasets in relation to BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA. For the BreastEW datasets, values of 0.9351, 0.9535, 0.9272, 0.8921, 0.6930, 0.9430 and 0.9149 were obtained for BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA, respectively. Interestingly, the BEOSA algorithm with a population size 100 yielded the best overall performance. The best overall performance obtained with the Exactly2, HeartEW and Ionosphere datasets was 0.7660, 0.8074 and 0.9286 using BPSO, BEOSA and BPSO, respectively. The breakdown results on the Exactly2 dataset showed classification accuracies of 0.7345, 0.7660, 0.7350, 0.7175, 0.6875, 0.5900, 0.7625 and 0.7495 when using  
BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA, respectively. Values of 0.6963, 0.8019, 0.7222, 0.6963, 0.5722, 0.4815, 0.8074 and 0.6870, and 0.8500, 0.9286, 0.9000, 0.8457, 0.8143, 0.7286, 0.9143 and 0.8729 were obtained for the BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA when using the HeartEW and Ionosphere datasets, respectively. Similarly, the performance of the Tic-tac-toe, Vote, Wine and Zoo datasets with BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA and BIEOSA was observed. The results showed with Tic-tac-toe, BEOSA was superior, with a classification accuracy of 0.7964. In contrast, the worst performance on this dataset was observed with the BDMO method, which yielded a value of 0.6219. The BEOSA method showed a classification accuracy of 0.9583 with the Vote dataset, a demonstration of superiority above all other methods; BPSO yielded 0.9450 and BDMO yielded 0.8333. The Wine and Zoo datasets yielded 0.9556 and 0.9400 classification accuracy with the BEOSA method for the two datasets. We note that in most cases where BEOSA outperformed the other methods, the population sizes were 75 and 100, which supports the attainment of optimal performance in the high dimensional datasets. **UTOHERE** The performance summary for all the methods showed that the BWOA algorithm operated with optimal classification accuracy on 10 datasets with 100 population size, while population sizes 25, 50 and 75 showed 0, 9 and 1 optimal classifications, respectively. The BPSO method demonstrated that using the population size of 100 resulted in 10 datasets performing very well. In contrast, the population sizes 25, 50 and 75 were only able to obtain the best performances, 2, 3 and 5, respectively. Similarly, we observed that the BSFO, BGWO, BDMO, and BSNDO obtained their best classification accuracy using the population sizes of 50, 75, 75, and 100 on 6, 9, 6, and 19 datasets, respectively. The BEOSA and BIEOSA methods obtained their best classification accuracy when the population sizes of 100 and 50 were used such that they both gave such best on 9 and 7 datasets, respectively. Meanwhile, BEOSA showed that using a population size of 25 and 50 will impair the performance, indicating that increased population size supports the improvement of the performance of the algorithm. The classification accuracy curves for the Zoo, Vote, Wine, Sonar, Tic-tac-toe, M-of-n, Ionosphere, Exactly2, ElectrBy, ElectrBy, CongressEW, Lymphography, Colon, BreastEW, and SpectEW datasets on the BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA, and BIEOSA are analyzed for further understanding of performance differences.