## 7 Results
### Comparative Study for the Method of Combined Classifiers  
In this section, the results obtained by the combination methods are described: Voting Majority Method (VMM) and Hierarchical Method (HM) described above. In the classification tests performed, we show how the combination of classification algorithms behaves in relation to the results obtained in the previous test using individual classifiers. For proper comparison, the same \(DM^{{}^{\prime\prime}}\) data used for individual classifiers, and the same classifier parameters were used in these analyses. In the first analysis, the Voting Majority Method (VMM) is applied using different combinations of three individual classification algorithms in parallel, the results obtained are shown in Tables 5-8. When analyzing the results from the tables, it can be seen, in both cases, that the detection of \(VF\) has significantly improved when compared with individual classifiers. However, the detection of \(VT\) has decreased when compared with what those obtained by the \(KNN\) algorithm, which was the best individual classifier. It is concluded that the combination of classifiers do not exceed the results obtained in case of \(KNN\). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{**BAGG**} & \multicolumn{4}{c}{**KNN**} \\ \cline{2-9}  & \(\mathbf{VF}\) & \(\mathbf{VT}\) & \(\mathbf{Others}\) & \(\mathbf{Normal}\) & \(\mathbf{VF}\) & \(\mathbf{VT}\) & \(\mathbf{Others}\) & \(\mathbf{Normal}\) \\ \hline \(VF\) & 1721 & 9 & 2 & 20 & 1666 & 52 & 13 & 21 \\ \(VT\) & 85 & 564 & 0 & 7 & 27 & 618 & 0 & 11 \\ \(Others\) & 4 & 1 & 1902 & 90 & 8 & 0 & 1954 & 35 \\ \(Normal\) & 16 & 3 & 6 & 4978 & 14 & 1 & 29 & 4949 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for classification of \(VF\), \(VT\), \(Others\), and \(Normal\) using the \(BAGG\) and \(KNN\) algorithms. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{**L2 RLR**} & \multicolumn{4}{c}{**ANNC**} \\ \cline{2-9}  & \(\mathbf{VF}\) & \(\mathbf{VT}\) & \(\mathbf{Others}\) & \(\mathbf{Normal}\) & \(\mathbf{VF}\) & \(\mathbf{VT}\) & \(\mathbf{Others}\) & \(\mathbf{Normal}\) \\ \hline \(VF\) & 1627 & 64 & 13 & 48 & 1689 & 34 & 3 & 26 \\ \(VT\) & 59 & 583 & 2 & 12 & 55 & 589 & 0 & 12 \\ \(Others\) & 9 & 0 & 1909 & 79 & 11 & 1 & 1941 & 44 \\ \(Normal\) & 32 & 8 & 68 & 4895 & 18 & 3 & 27 & 4955 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for classification of \(VF\), \(VT\), \(Others\), and \(Normal\) using the \(L2 RLR\) and \(ANNC\) algorithms. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{_Others_} & \multicolumn{4}{c}{_Normal_} \\ \cline{2-9}  & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** \\ \hline
**Combined Classification Algorithms (Cla, Clb, Clc)** & _Others_ & **Global** & **Global** & **Global** & **Normal** & **Global** & **Global** \\ \hline \hline \(\Delta\)NNC\_KNN\_l2 RLR_ & \(97.35\pm 0.15\) & \(99.67\pm 0.02\) & \(99.16\pm 0.03\) & \(98.57\pm 0.05\) & \(99.22\pm 0.05\) & \(98.41\pm 0.09\) & \(98.84\pm 0.03\) & \(98.63\pm 0.02\) \\ \hline
**ANNC\_KNN\_BAGG** & \(97.20\pm 0.11\) & \(99.84\pm 0.04\) & \(99.27\pm 0.01\) & \(99.21\pm 0.14\) & \(99.52\pm 0.06\) & \(98.29\pm 0.05\) & \(98.95\pm 0.01\) & \(98.64\pm 0.04\) \\ \hline
**BAGG\_KNN\_l2 RLR** & \(96.95\pm 0.11\) & \(99.77\pm 0.04\) & \(99.16\pm 0.01\) & \(99.25\pm 0.18\) & \(99.4\pm 0.05\) & \(98.30\pm 0.03\) & \(98.89\pm 0.01\) & \(98.51\pm 0.02\) \\ \hline
**BAGG\_ANNC\_l2 RLR** & \(96.54\pm 0.09\) & \(99.74\pm 0.05\) & \(99.05\pm 0.02\) & \(99.12\pm 0.17\) & \(99.38\pm 0.09\) & \(97.68\pm 0.23\) & \(98.59\pm 0.09\) & \(98.26\pm 0.17\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for the VMM combination method using \(TRI\) as data input. Results correspond to detection of _Other_ and _Normal_ classes.