## 4 The Proposed Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{5}{c}{**Feature Extracted Dimension**} \\ \cline{2-7}  & \multicolumn{2}{c}{**FEF\_100F**} & \multicolumn{2}{c}{**SEF\_50F**} & \multicolumn{2}{c}{**SEF\_500F**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Error Rate (\%)**} & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Error Rate (\%)**} & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Error Rate (\%)**} \\ \hline GDA & 95.65 & 4.35 & 96.54 & 3.46 & 95.90 & 4.10 \\ \hline KNN & 95.77 & 4.23 & 96.93 & 3.07 & 95.26 & 4.74 \\ \hline LOG & 92.69 & 7.31 & 96.29 & 3.71 & 92.06 & 7.94 \\ \hline SVM & 95.13 & 4.87 & 96.16 & 3.84 & 95.00 & 5.00 \\ \hline RF & 96.29 & 3.71 & 96.67 & 3.72 & 95.20 & 4.80 \\ \hline ELM & 70.42 & 29.58 & 93.85 & 6.15 & 94.37 & 5.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation measure results of DenseNet201 features and a number of different classifiers over the Malimg dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{5}{c}{**Feature Extracted Dimension**} \\ \cline{2-7}  & \multicolumn{2}{c}{**FEF\_100F**} & \multicolumn{2}{c}{**SEF\_50F**} & \multicolumn{2}{c}{**SEF\_500F**} \\ \hline  & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Error Rate (\%)**} & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Error Rate (\%)**} & \multicolumn{2}{c}{**Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Error Rate (\%)**} \\ \hline GDA & 96.41 & 3.59 & 97.06 & 2.94 & 96.03 & 3.97 \\ \hline KNN & 94.49 & 5.51 & 95.90 & 4.10 & 95.02 & 4.98 \\ \hline LOG & 95.01 & 4.99 & 96.15 & 3.85 & 95.65 & 4.35 \\ \hline SVM & 95.52 & 4.48 & 96.03 & 3.97 & 95.13 & 4.87 \\ \hline RF & 94.11 & 5.89 & 95.77 & 4.23 & 94.49 & 5.51 \\ \hline ELM & 66. The findings can be examined in terms of the optimal feature and classifier, and this will be discussed in the next section. We decided to start at the feature dimension of 50 and stop at the feature dimension of 500 based on the findings. We chose the FEFs and SEFs of the ResNet50 model as examples. As can be seen in Figure 12, there is no requirement for us to proceed above size 500 because the accuracy ratio at (SEF_50F) increased (96.54%) but decreased until it reached (95.77%) at size 500. Furthermore, we can observe that the accuracy ratio at FEF_1000F is 95.90%, but the accuracy is 96.54% when we utilize SEF_50F. This demonstrates how feature selection helps enhance performance. When comparing the accuracy of five pretrained models to various feature selection dimensions, SEF_50F consistently obtains superior accuracy. Therefore, we considered SEF_50F for carrying out additional experiments for the purpose of this paper. Performance Analysis of Each Classifier

An analysis of the findings in Tables 5-9 will make selecting the optimal classifier possible. With the purpose of applying the assessment metrics, a comparison across all classifiers will be carried out, including accuracy and error. One of the things that we are conducting right now is determining the overall mean for each metric for all features for each classifier that we are currently testing. It was discovered that the average accuracy values for KNN, GDA, LOG, SVM, RF, and ELM are 96.49, 96.53, 96.26, 95.28, 96.05, and 94.15, respectively. When compared to all of the classifiers, the KNN classifier was able to obtain the maximum accuracy rate in regard