# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Feature Selection  
Feature selection (FS) is a technique for handling high-dimensional data. In other words, it is a preprocessing stage applied to datasets as a dimensionality reduction strategy for prediction or classification by eliminating redundant and unnecessary features. Furthermore, it helps to reduce the computational time and enhances the classification accuracy. In FS, a binary vector composed of zero and one is generated. A value of one corresponds to a selected feature, whereas a value of zero indicates that the associated feature is unselected. Consequently, the EJAYA population is generated as a vector containing values of either zero or one. Therefore, the classification accuracy and the number of selected features are compared to various population-based FS approaches, such as the GA, ALO, and a combination of whale optimization algorithms and simulated annealing (SAWOASAT-2) [73]. Table 5 summarizes a variety of standard University of California, Irvine (UCI) datasets used to test the proposed approach for feature selection. Figure 4: The convergence rate of different algorithms from F13 to F20. The findings of EJAYA in terms of the classification accuracy on eighteen UCI standard datasets are presented in Table 6. The table highlights the best outcomes in bold. EJAYA demonstrates impressive classification accuracies on eleven datasets, including Breast Cancer, Congress EW, Exactly2, Heart EW, Lymphography, Peng Lung EW, Sonar EW, Tic-Tac-Toe, Waveform EW, Wine EW, and Zoo, as evidenced by the experimental results. Moreover, EJAYA's accuracy rates are comparable to other algorithms on six datasets, including Exactly, Ionosphere EW, Kraske EW, M-of-N, Spect EW, and Vote. However, it is less accurate than others on the Breast EW dataset. Notably, EJAYA's average classification accuracy is more remarkable than other approaches, indicating that it is the most efficient in finding the optimal solution, achieving an accuracy rate of 0.93. As a result, EJAYA surpasses the other FS techniques in terms of accuracy across all datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **WOASAT-2** & **ALO** & **GA** & **PSO** & **FULL** & **EJAYA** \\ \hline Breast cancer & 0.97 & 0.96 & 0.96 & 0.95 & 0.94 & **0.98** \\ Breast EW & **0.98** & 0.93 & 0.94 & 0.94 & 0.96 & 0.97 \\ Congress EW & 0.98 & 0.93 & 0.94 & 0.94 & 0.92 & **0.99** \\ Exactly & **1.00** & 0.66 & 0.67 & 0.68 & 0.67 & **1.00** \\ Exactly2 & 0.75 & 0.75 & 0.76 & 0.75 & 0.74 & **0.76** \\ Heart EW & 0.85 & 0.83 & 0.82 & 0.78 & 0.82 & **0.86** \\ Ionosphere EW & **0.96** & 0.87 & 0.83 & 0.84 & 0.87 & **0.96** \\ Krvskp EW & **0.98** & 0.96 & 0.92 & 0.94 & 0.92 & **0.98** \\ Lymphography & 0.89 & 0.79 & 0.71 & 0.69 & 0.68 & **0.90** \\ M-of-n & **1.00** & 0.86 & 0.93 & 0.86 & 0.85 & **1.00** \\ Penglung EW & 0.94 & 0.63 & 0.70 & 0.72 & 0.66 & **1.00** \\ Sonar EW & 0.97 & 0.74 & 0.73 & 0.74 & 0.62 & **0.99** \\ Spect EW & **0.88** & 0.80 & 0.78 & 0.77 & 0.83 & **0.88** \\ Tic-tac-toe & 0.79 & 0.73 & 0.71 & 0.73 & 0.72 & **0.80** \\ Vote & **0.97** & 0.92 & 0.89 & 0.89 & 0.88 & **0.97** \\ Waveform EW & 0.76 & 0.77 & 0.77 & 0.76 & 0.77 & **0.78** \\ Wine EW & 0.99 & 0.91 & 0.93 & 0.95 & 0.93 & **1.00** \\ Zoo & 0.97 & 0.91 & 0.88 & 0.83 & 0.79 & **1.00** \\ Average & 0.92 & 0.83 & 0.83 & 0.82 & 0.81 & **0.93** \\ \hline \hline \end{tabular} The better results were shown in bold format.  
\end{table} 
Table 6: The assessment of different algorithms in terms of the accuracy.