## 5 Results and Discussion of Findings
### Discussion of Findings  
As corroborated by the obtained results and discussed in detail in previous subsections, this study may conclude that the proposed BEOSA method demonstrated very promising performance on all benchmark datasets. We have shown that this method produced the optimal average number of selected features on each of the tested datasets. Furthermore, we discovered that the popular classifiers KNN, MLP, SVM, GNB and RF were relevant in terms of supporting the performance of binary optimizers. This motivates designers of binary optimizers to investigate which state-of-the-art classifier is suitable for supporting particular wrapper-based feature selection and classification tasks. Moreover, when a classifier impedes the performance of a binary optimizer, it diminishes the importance of using the algorithm for optimization purposes. Hence, deploying such binary optimizers to real problems must be accompanied by the selection of an appropriate classifier. The average numbers of feature selected for all datasets using the proposed BEOSA demonstrated that the algorithm is suitable for maximizing the cost function and minimizing the fitness function. Our findings also confirm that the novel method used for applying the S-functions and V-functions enhanced the performance of the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **BWOA** & **BPSO** & **BSFO** & **BGWO** & **BDMO** & **BSNDO** & **BEOSA** & **BIEOSA** \\ \hline BreastEW & 2863.07 & 5716.95 & 15,231.68 & 3284.61 & 4693.94 & 0.05 & 7755.12 & 7143.08 \\ Lung & 10,836.27 & 6407.44 & NA & 13,519.02 & 21,901.97 & 0.06 & 27,005.21 & 27,469.88 \\ CongressEW & 2494.41 & 3293.75 & 21,216.45 & 2911.41 & 3167.04 & 0.05 & 3681.29 & 3439.88 \\ Exactly & 4238.07 & 5576.70 & 37,823.17 & 4967.03 & 5139.55 & 0.05 & 6135.80 & 5239.17 \\ Iris & 1539.72 & 1569.79 & 0.0300 & 1248.06 & 2454.02 & 0.05 & 3638.87 & 4367.40 \\ Exactly2 & 4894.75 & 5460.21 & 84,742.30 & 5886.60 & 5831.48 & 0.05 & 5629.12 & 5949.60 \\ HeartEW & 5991.47 & 6180.32 & 11,961.87 & 6103.14 & 4953.76 & 0.05 & 8289.17 & 7775.06 \\ Ionosphere & 2425.15 & 5192.26 & 26,648.48 & 2711.81 & 4576.30 & 0.06 & 6795.18 & 5838.63 \\ Prostate & 1399.96.86 & 237,15.34 & 0.050 & 1962.22 & 18,691.10 & 0.05 & 14,753.88 & 14,729.31 \\ Lymphography & 7088.64 & 4101.17 & 15,600.77 & 3363.44 & 4113.92 & 249.905 & 6342.72 & 6345.36 \\ M-of-n & 3377.35 & 4705.02 & 56,589.18 & 4243.87 & 4042.42 & 0.05 & 5927.31 & 4770.37 \\ Leukemia & 12,557.96 & 15,367.75 & NA & 15,879.91 & 14,626.92 & 0.05 & 10,973.17 & 13,082.37 \\ PenglungEW & 972.86 & 1186.14 & 7022.13 & 1027.13 & 1057.12 & 1613.04 & 1570.83 & 1238.32 \\ Sonar & 2478.74 & 3265.90 & 23,013.84 & 2694.13 & 4308.82 & 0.06 & 4640.60 & 4789.33 \\ SpectEW & 2809.20 & 3386.75 & 15,919.29 & 3107.25 & 3314.52 & 0.04 & 4389.90 & 4118.12 \\ Colon & 9003.78 & 10,541.57 & NA & 11,003.80 & 10,026.80 & 0.06 & 8971.40 & 9562.65 \\ Tic-toe-toe & 7769.29 & 9578.16 & 54,646.60 & 8591.51 & 7463.63 & 0.05 & 13,032.30 & 10,677.70 \\ Vote & 2430.62 & 2837.52 & 24,196.79 & 2561.77 & 2947.24 & 0.05 & 44046.51 & 4197.18 \\ Wine & 2774.64 & 4566.93 & 19,463.48 & 3309.42 & 5377.52 & 0.05 & 7005.92 & 8468.67 \\ Zoo & 2013.56 & 2427.55 & 8370.56 & 2118.25 & 2465.75 & 0.05 & 3384.51 & 3437.60 \\ KrVskpEW & 8482.48 & 17,028.06 & 173,356.6 & 12,685.90 & 20,658.05 & 0.07 & 25,079.98 & 18,698.16 \\ WaveformEW & 17,449.58 & 32,664.62 & NA & 21,421.47 & 22,991.37 & NA & 26,812.95 & 27,680.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of the computational times of BWOA, BPSO, BSFO, BGWO, BDMO, BSNDO, BEOSA, and BIEOSA. Research on the optimization of the number of selected features for classification operations is aimed at obtaining the best optimizer. It is expected that such an optimizer will advance research in the field by ensuring that while classification accuracy is maximized, the number of features must be as low as possible.