## 3 Evaluation
### Experimental Setup
#### 3.2.2 Classification Setting
We also conducted experiments on classifiers used in [48] and added their results on the WDBC dataset in Table 4. The used classifiers were SESSC as a standalone classifier, ESSC_LSE, and SESSC_LSE (the ESSC and the SESSC clustering algorithms integrated with a fuzzy classifier, respectively). We can notice that again using our model to transform the test set improves their performance as other classifiers, which further proves the pluggable feature of our model. \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Classifier** & **Train Set** & **Test Set** & **Average Accuracy** & **Average Loss** \\ \hline \multirow{4}{*}{MNIST} & NN & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{S}}}\) & 0.829278 & 0.588378 \\  & NN & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.890600** & **0.423493** \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.747853 & - \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.866773** & - \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Fashion- \\ MNIST \\ \end{tabular} } & NN & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.780324 & 0.652228 \\  & LSVM & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.809889** & **0.648123** \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.709376 & - \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.780488** & - \\ \hline \multirow{4}{*}{USPS} & NN & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.905552 & 0.328199 \\  & LSVM & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.938645** & **0.245099** \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.861530 & - \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.937175** & - \\ \hline \multirow{4}{*}{MSTAR} & NN & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.721635** & **0.528935** \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.641397 & 0.620295 \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.904600 & - \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.980579** & - \\ \hline \multirow{4}{*}{WDBC} & NN & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.976023 & 0.101761 \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.988304** & **0.034921** \\  & LSVM & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.964912 & - \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.988304** & - \\  & & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.949708 & - \\  & ESSC & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.985965** & - \\  & SESSC & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.954386 & - \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.992398** & - \\  & SESSC & \(D^{\mathcal{H}}\) & \(D^{\mathcal{I}_{\text{T}}}\) & 0.971930 & - \\  & & \(\{D^{\mathcal{H}},\,D^{\mathcal{I}_{\text{T}}}\}\) & \(D^{\mathcal{I}_{\text{T}}}\) & **0.990058** & - \\ \hline \end{tabular}
\end{table} 
Table 4: Classification results. \(D^{\mathcal{H}}\) is the original training set (without transformation) and \(\{D^{\mathcal{H}},\,D^{\mathcal{H}}\}\) is the original training \(D^{\mathcal{H}}\), \(D^{\mathcal{I}_{\text{T}}}\), \(D^{\mathcal{I}_{\text{T}}}\), Bolded values indicate the best results achieved in each metric. In Set-2 of the pilot experiments, we compared two options. Option 1 was using the reconstructions of the third module as input to the classifier versus the original input data (as demonstrated before).