## 3 Evaluation
### Experimental Setup
#### 3.2.2 Classification Setting
Option 2 was proceeding to the applied classifier with the output of the second module, which were the latent representations obtained from the regressors, versus using the latent representations obtained directly from the autoencoder. Results for semi-supervised clustering and classification are shown in Tables 6 and 7, respectively. Bolded values indicate the best results obtained in each metric except for Inertia where bolded value indicate the best results obtained for each option separately as Inertia is not a normalized metric. The results show that Option 1 is slightly better than Option 2 in most cases in both semi-supervised and supervised settings. In addition, the reconstruction option has the added privilege of not interfering with any subsequent machine learning task. Because the output is of the same dimensionality and nature of the input data, (i.e., no changes are needed for the baseline classification and clustering model setups). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**K-Means**} & \multirow{2}{*}{**Option**} & \multirow{2}{*}{**Test Set**} & \multirow{2}{*}{_Time_} & \multirow{2}{*}{_Inertia_} & \multirow{2}{*}{_C_} & \multirow{2}{*}{_V\({}_{\beta}\)_} & \multirow{2}{*}{_ARI_} & \multirow{2}{*}{_AMI_} & \multirow{2}{*}{_S_} \\  & **Init. Method** & & & & & & & & & \\ \hline \multirow{8}{*}{MNIST} & \multirow{4}{*}{K-Means++} & Option1 & \(D^{\text{Ts}}\) & 119.802 & 42,141,554 & 0.421 & 0.443 & 0.432 & 0.322 & 0.421 & 0.058 \\  & & & s & & & & & & & \\  & & \(D^{\text{Ts}}\) & 32.010 s & **14,454,470** & 0.658 & **0.680** & 0.669 & **0.586** & 0.658 & **0.396** \\  & & Option2 & \(Z_{\text{ts}}\) & 19.931 s & 14,528,156 & 0.537 & 0.544 & 0.541 & 0.418 & 0.537 & 0.063 \\  & & \(Z_{\text{ts}}\) & **15.100 s** & **6,730,338** & **0.666** & 0.676 & **0.671** & 0.570 & **0.666** & 0.329 \\ \hline \multirow{8}{*}{MNIST} & \multirow{4}{*}{Random} & Option1 & \(D^{\text{Ts}}\) & 62.014 s & 42,142,078 & 0.417 & 0.439 & 0.428 & 0.318 & 0.417 & 0.060 \\  & & \(D^{\text{Ts}}\) & 31.006 s & **14,454,470** & 0.658 & **0.680** & 0.669 & **0.586** & 0.658 & **0.381** \\  & & Option2 & \(Z_{\text{ts}}\) & 18.312 s & 14,510,545 & 0.526 & 0.535 & 0.531 & 0.397 & 0.526 & 0.066 \\  & & \(Z_{\text{ts}}\) & **11.168 s** & **6,730,342** & **0.666** & 0.676 & **0.671** & 0.570 & **0.666** & 0.329 \\ \hline \multirow{8}{*}{PCA-Based} & Option1 & \(D^{\text{Ts}}\) & 14.302 s & 42,142,060 & 0.417 & 0.439 & 0.428 & 0.317 & 0.417 & 0.055 \\  & & \(D^{\text{Ts}}\) & 8.425 s & **14,454,470** & **0.658** & **0.680** & 0.669 & **0.586** & 0.658 & **0.394** \\ \cline{1-1}  & & Option2 & \(Z_{\text{ts}}\) & 4.908 s & 14,510,560 & 0.526 & 0.535 & 0.531 & 0.397 & 0.526 & 0.062 \\ \cline{1-1}  & & \(Z_{\text{ts}}\) & **3.618 s** & **6,847,240** & 0.655 & 0.664 & 0.659 & 0.567 & 0.655 & 0.262 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Set-2 pilot experiment semi-supervised clustering setting results. \(D^{\text{Ts}}\) is the original unlabeled test set (without transformation), \(D^{\text{Ts}}\) is the transformed (using our model) unlabeled test set. \(Z_{\text{ts}}\) and \(Z_{\text{ts}}\) are the features extracted from the autoencoder of the original test set \(D^{\text{Ts}}\) and the transformed test set \(D^{\text{Ts}}\), respectively. Bolded values indicate the best results achieved in each metric. Figure 5: Set-1 pilot experiment accuracies on the MNIST and Fashion-MNIST datasets. In addition, to further prove the effectiveness of our proposed method, we have provided a comparison with different conventional methods that work on the improvement of classification accuracy of the two naturally small datasets used in this research (WDBC and MSTAR). First, we compared the WDBC dataset to other existing methods that improve the classification accuracy over the dataset using different techniques, mostly feature selection. The WDBC dataset was first normalized and then split into 70-30% training and testing sets.