## 7 Results
### Comparative Study for the Method of Combined Classifiers
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{\(\boldsymbol{ANNC\_KNN\_L2 RLR}\)} & \multicolumn{4}{c}{\(\boldsymbol{ANNC\_KNN\_BAGG}\)} \\ \cline{2-10}  & \(\boldsymbol{VF}\) & \(\boldsymbol{VT}\) & \(\boldsymbol{Others}\) & \(\boldsymbol{Normal}\) & \(\boldsymbol{VF}\) & \(\boldsymbol{VT}\) & \(\boldsymbol{Others}\) & \(\boldsymbol{Normal}\) \\ \hline \(VF\) & 1706 & 23 & 5 & 18 & 1720 & 17 & 2 & 13 \\ \(VT\) & 45 & 600 & 1 & 10 & 53 & 595 & 0 & 8 \\ \(Others\) & 8 & 0 & 1948 & 41 & 6 & 1 & 1943 & 47 \\ \(Normal\) & 14 & 0 & 23 & 4965 & 14 & 1 & 15 & 4973 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix for classification of \(VF\), \(VT\), \(Others\), and \(Normal\) classes using the \(ANNC\_KNN\_L2 RLR\) and \(ANNC\_KNN\_BAGG\) VMM combined classifier. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{\(\boldsymbol{BAGG\_KNN\_L2 RLR}\)} & \multicolumn{4}{c}{\(\boldsymbol{BAGG\_ANNC\_L2 RLR}\)} \\ \cline{2-9}  & \(\boldsymbol{VF}\) & \(\boldsymbol{VT}\) & \(\boldsymbol{Others}\) & \(\boldsymbol{Normal}\) & \(\boldsymbol{VF}\) & \(\boldsymbol{VT}\) & \(\boldsymbol{Others}\) & \(\boldsymbol{Normal}\) \\ \hline \(VF\) & 1724 & 14 & 0 & 14 & 1717 & 13 & 2 & \\ \(VT\) & 55 & 595 & 0 & 6 & 62 & 586 & 0 & 8 \\ \(Others\) & 6 & 1 & 1935 & 55 & 7 & 0 & 1930 & 60 \\ \(Normal\) & 14 & 1 & 14 & 4974 & 15 & 1 & 16 & 4971 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix for classification of \(VF\), \(VT\), \(Others\), and \(Normal\) classes using the \(BAGG\_KNN\_L2 RLR\) and \(BAGG\_ANNC\_L2 RLR\) VMM combined classifier. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{_Others_} & \multicolumn{4}{c}{_Normal_} \\ \cline{2-10}  & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** \\ \hline
**Combined Classification Algorithms (Cls, Cls, Cls)** & _Others_ & **Global** & **Global** & **Global** & **Normal** & **Global** & **Global** & **Global** \\ \hline _ANN\_CNN\_ANNC_ & & & & & & & & & \\ Test & 97.22 \(\pm\) 0.14 & 99.57 \(\pm\) 0.11 & 99.06 \(\pm\) 0.10 & 98.45 \(\pm\) 0.39 & 99.04 \(\pm\) 0.15 & 98.10 \(\pm\) 0.26 & 98.61 \(\pm\) 0.15 & 98.39 \(\pm\) 0.22 \\ \hline _ANN\_CNN\_2 RLR_ & & & & & & & \\ Test & 96.11 \(\pm\) 0.67 & 99.18 \(\pm\) 0.33 & 98.45 \(\pm\) 0.38 & 96.70 \(\pm\) 1.16 & 98.42 \(\pm\) 0.43 & 97.67 \(\pm\) 0.26 & 98.17 \(\pm\) 0.30 & 98.01 \(\pm\) 0.22 \\ \hline _BAG\_CNN\_ANNC_ & & & & & & & & \\ Test & 97.42 \(\pm\) 0.12 & 99.52 \(\pm\) 0.07 & 99.07 \(\pm\) 0.08 & 98.25 \(\pm\) 0.29 & 99.02 \(\pm\) 0.14 & 98.36 \(\pm\) 0.10 & 98.72 \(\pm\) 0.11 & 98.59 \(\pm\) 0.09 \\ \hline _BAG\_CNN\_12 RLR_ & & & & & & & & \\ Test & 96.92 \(\pm\) 0.38 & 98.92 \(\pm\) 0.05 & 98.28 \(\pm\) 0.04 & 96.10 \(\pm\) 0.17 & 98.22 \(\pm\) 0.07 & 97.73 \(\pm\) 0.19 & 97.99 \(\pm\) 0.05 & 98.06 \(\pm\) 0.16 \\ \hline _I2 RLR\_KNN\_ANNC_ & & & & & & & & \\ Test & 97.22 \(\pm\) 0.11 & 99.50 \(\pm\) 0.06 & 99.01 \(\pm\) 0.07 & 98.20 \(\pm\) 0.24 & 98.61 \(\pm\) 0.14 & 97.39 \(\pm\) 0.27 & 98.05 \(\pm\) 0.18 & 97.79 \(\pm\) 0.23 \\ \hline _I2 RLR\_KNN\_12 RLR_ & & & & & & & & \\ Test & 95.71 \(\pm\) 0.30 & 98.84 \(\pm\) 0.02 & 98.16 \(\pm\) 0.04 & 95.83 \(\pm\) 0.07 & 97.81 \(\pm\) 0.08 & 96.89 \(\pm\) 0.32 & 97.38 \(\pm\) 0.10 & 97.34 \(\pm\) 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results for different HM combined classifier methods to detect _Normal_ and _Others_ classes.