## 4 Data Preparation and Results
### Results and Discussions
For instance, the number of the Kidney Renal Clear Cell Carcinoma cases (class 1) is far greater than that of the Rhabdoid Tumor cases (class 5); refer to Table 1. Therefore, the training set for each class needs to be balanced to obtain unbiased classification results. For this purpose, a data augmentation procedure, given in [32], was applied. Small, random Gaussian noise with zero Mean and 0.02 variance was added to the data points of those classes with fewer cases, resulting in a balanced dataset. It is essential that the test set samples are removed before data augmentation. One-fifth of each subtype dataset was randomly selected and reserved for this purpose in a five-fold validation process, and then the training set was augmented to have 475 training samples for each subtype set. This process was repeated 10 times to archive the \(10\times 5\)-Cross Validation, and the results were averaged to construct the confusion matrices as shown in Figures 8 and 9. Figure 8: Classification accuracy of balanced training of 1627 miRNAs. Figure 7: Classification results using the 35 selected miRNAs as features. To further assess the classification performance, Matthews Correlation Coefficient (MCC) [6] was adopted. The MCC is given by the following equation:  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{12}\]  
In order to compute MCC, the true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), and false negative (\(FN\)) were extracted as shown in Tables 3 and 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Selected 35 miRNA for**} & \multicolumn{4}{c}{**All 1627 miRNA for**} \\  & \multicolumn{4}{c}{**Unbalanced Classes**} & \multicolumn{4}{c}{**Unbalanced Classes**} \\ \hline
**Class** & **TP** & **FP** & **FN** & **TN** & **TP** & **FP** & **FN** & **TN** \\ \hline WT & 1309 & 41 & 71 & 10,789 & 1340 & 38 & 40 & 10,792 \\ KICH & 896 & 178 & 14 & 11,122 & 877 & 74 & 33 & 11,226 \\ KIRC & 5920 & 68 & 240 & 5982 & 6015 & 71 & 145 & 5979 \\ KIRP & 3044 & 179 & 216 & 8771 & 3169 & 113 & 91 & 8837 \\ RT & 477 & 98 & 23 & 11,612 & 467 & 46 & 33 & 11,664 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The extracted true positive, true negative, false positive, and false negative from the balanced dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Selected 35 miRNA for**} & \multicolumn{4}{c}{**All 1627 miRNA for**} \\  & \multicolumn{4}{c}{**Unbalanced Classes**} & \multicolumn{4}{c}{**Unbalanced Classes**} \\ \hline
**Class** & **TP** & **FP** & **FN** & **TN** & **TP** & **FP** & **FN** & **TN** \\ \hline WT & 1331 & 40 & 48 & 10,781 & 1311 & 70 & 69 & 10,750 \\ KICH & 818 & 112 & 91 & 11,179 & 769 & 174 & 139 & 11,118 \\ KIRC & 5992 & 140 & 165 & 5903 & 5824 & 260 & 334 & 5782 \\ KIRP & 3068 & 239 & 187 & 8706 & 2985 & 320 & 270 & 8625 \\ RT & 440 & 20 & 60 & 11,680 & 444 & 43 & 55 & 11,658 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The extracted true positive, true negative, false positive, and false negative from the unbalanced dataset. Figure 9: Classification accuracy of balanced training of 35 selected miRNAs by NCA. The multiclass generalization of the MCC, refer to [33], for the cells of the confusion matrix \(C\) is given in Table 5. The proposed method was able to achieve an average classification accuracy of 97.2% and an MCC of 0.949 if all the available miRNAs were used and if the dataset was balanced. If not balanced, the accuracy is reduced to 92.9% and the MCC value to 0.887. With the 35 selected miRNA, the accuracy was 95.4% for both and the MCC values were 0.92 and 0.924, respectively (Figures 6-9 and Table 5). It is clear that using the selected set of miRNAs, one can achieve a more consistent performance, in terms of both classification accuracy and MCC [34]. The result also shows that the selected 35 miRNAs performed better without data augmentation, compared to those obtained by using all of the available miRNAs (Table 5).