# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## 10 Results and Discussion
The accuracy rank results of the Friedman test for each algorithm are shown in Table 5. According to overall ranks, IWSO outperforms others, and it is followed by GWO, PSO, OWSO, DA, GWO, and ALO in that order. When evaluating the accuracy results across all datasets, it is determined that IWSO does not achieve the best performance on the Lymphography and Dermatology datasets. It is noteworthy that these datasets have more than three classes. IWSO can be preferred over another approach when the class size is below four, according to experimental studies. Therefore, it is suggested that the performance of IWSO should be improved, particularly for datasets with a larger number of classes. The relatively lower performance of IWSO on multi-class datasets can be attributed to increased class overlap and more complex decision boundaries, which challenge both ELM's representational capacity and the war strategy optimizer's convergence ability. In contrast, traditional methods like PSO and GWO may excel in simpler or low-dimensional datasets due to their stronger local search behavior. Furthermore, the fixed structure and \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **IWSO vs. ALO** & **IWSO vs. DA** & **IWSO vs. PSO** & **IWSO vs. GWO** & **IWSO vs. WSO** \\ \hline \(p\) value & \(2.08\times 10^{-38}\) & \(1.40\times 10^{-29}\) & \(6.95\times 10^{-20}\) & \(1.98\times 10^{-10}\) & \(1.68\times 10^{-28}\) & \(2.10\times 10^{-17}\) \\ \hline h & + & + & + & + & + & + \\ \hline \end{tabular}
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Table 4: Results of the Wilcoxon rank sum test. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**ALO**} & \multicolumn{2}{c}{**DA**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GWO**} & \multicolumn{2}{c}{**IWSO**} \\ \cline{2-11}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** \\ \hline Breast & 97.35 & 0.97 & 98.25 & 0.83 & 98.64 & 0.76 & 98.53 & 0.86 & * 98.71 & 0.62 \\ \hline Bupa & 78.83 & 3.70 & 79.87 & 3.10 & 79.00 & 3.45 & 81.26 & 3.82 & * 81.42 & 3.24 \\ \hline Dermatology & 92.27 & 3.58 & 95.98 & 2.77 & 98.32 & 1.14 & 99.63 & 0.63 & * 99.60 & 0.59 \\ \hline Diabetes & 75.28 & 2.33 & 75.83 & 1.99 & 75.26 & 1.72 & 78.38 & 3.04 & * 80.91 & 2.04 \\ \hline Hepatitis & 96.67 & 2.98 & 97.08 & 2.71 & 98.47 & 2.04 & 97.36 & 2.56 & * 99.03 & 1.79 \\ \hline Lymphography & 92.05 & 3.03 & 94.39 & 2.51 & * 96.82 & 2.28 & 95.45 & 3.16 & 94.39 & 2.51 \\ \hline Parkinsons & 87.99 & 2.62 & 88.56 & 2.92 & 89.48 & 2.65 & 88.97 & 2.25 & * 92.76 & 1.72 \\ \hline SAheart & 74.64 & 1.56 & 75.65 & 2.46 & 76.18 & 1.83 & 78.26 & 3.54 & * 79.06 & 2.60 \\ \hline SPECTF & 84.63 & 2.75 & 84.79 & 1.89 & 84.83 & 1.79 & 84.92 & 2.95 & * 87.42 & 2.95 \\ \hline Vertebral & 87.67 & 2.24 & 88.14 & 1.97 & 88.17 & 2.00 & 89.82 & 3.25 & * 90.00 & 2.04 \\ \hline WDBC & 94.06 & 1.64 & 94.59 & 1.60 & 94.84 & 1.38 & 94.92 & 1.46 & * 98.14 & 1.05 \\ \hline Overall Mean & 87.40 & & 88.47 & & 89.09 & & 89.77 & * 91.04 & \\ \hline Overall Rank & 5 & 4 & 3 & & 2 & & 1 & \\ \hline \end{tabular}
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Table 3: Results of classification accuracy of the datasets. global exploration bias of IWSO may not always adapt well to the varying non-linear separability present in different classification tasks. IWSO tends to perform more consistently on binary or low-class datasets; however, in certain cases, such as the Lymphography dataset, traditional optimizers like PSO achieved slightly better results, possibly due to simpler decision boundaries and strong local search capabilities. ELM has been adopted as a classification model with proven reliability and success for small datasets. The aim of this paper is to use the proposed novel random opposition-based learning strategy with a recent metaheuristic algorithm, WSO. For this purpose, first the standard WSO and then the OWSO algorithm that has the classical opposition-based learning strategy were implemented.