## 4 Results
### Performance Comparisons between DPGA and Grid-Search Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **DR** & **FFE** & **RFE** & **PCA** & **Parameters** \\ \hline \multirow{4}{*}{FD001} & EGS-MLP & NA & ES & None & False & \(s_{\text{FWNO}}=\text{{\it bfgs}}\), \(n_{\text{HN}}=20\), \(\text{{\it acrf}}_{\text{HL}}=\text{{\it relu}}\) \\  & EGS-KNN & NA & SMA & None & False & \(n_{\text{HN}}=\text{{\it 4}}\), \(\text{{\it wf}}_{\text{HN}}=\text{{\it distance}}\) \\  & EGS-SVM & NA & ES & None & False & \(C=8\) \\  & EGS-RF & NA & ES & None & False & \(n_{\text{HW}}=10\) \\ \hline \multirow{4}{*}{FD002} & EGS-MLP & NA & ES & None & False & \(s_{\text{FWNO}}=\text{{\it adan}}\), \(n_{\text{HN}}=18\), \(\text{{\it acrf}}_{\text{HL}}=\text{{\it tanh}}\) \\  & EGS-kNN & NA & EMA & None & False & \(n_{\text{HN}}=10\), \(\text{{\it erf}}_{\text{HN}}=\text{{\it distance}}\) \\  & EGS-SVM & NA & ES & None & False & \(C=6\) \\  & EGS-RF & NA & ES & None & False & \(n_{\text{HW}}=10\) \\ \hline \multirow{4}{*}{FD003} & EGS-MLP & NA & ES & None & False & \(s_{\text{FWNO}}=\text{{\it bfgs}}\), \(n_{\text{HN}}=20\), \(\text{{\it acrf}}_{\text{HL}}=\text{{\it relu}}\) \\  & EGS-kNN & NA & SMA & None & False & \(n_{\text{HN}}=8\), \(\text{{\it wf}}_{\text{HN}}=\text{{\it distance}}\) \\  & EGS-SVM & NA & ES & None & False & \(n_{\text{HW}}=6\) \\ \hline \multirow{4}{*}{FD004} & EGS-MLP & NA & ES & None & False & \(s_{\text{FWNO}}=\text{{\it bfgs}}\), \(n_{\text{HN}}=20\), \(\text{{\it acrf}}_{\text{HL}}=\text{{\it relu}}\) \\  & EGS-SVM & NA & ES & None & False & \(n_{\text{HN}}=8\), \(\text{{\it wf}}_{\text{HN}}=\text{{\it distance}}\) \\  & EGS-SVM & NA & ES & None & False & \(n_{\text{HW}}=6\) \\ \hline \multirow{4}{*}{FD004} & EGS-MLP & NA & ES & None & False & \(s_{\text{FWNO}}=\text{{\it bfgs}}\), \(n_{\text{HN}}=15\), \(\text{{\it acrf}}_{\text{HL}}=\text{{\it relu}}\) \\  & EGS-kNN & NA & None & None & False & \(n_{\text{HN}}=10\), \(\text{{\it erf}}_{\text{HN}}=\text{{\it distance}}\) \\  & EGS-kNN & NA & ES & None & False & \(C=8\) \\ \cline{2-8}  & EGS-RF & NA & ES & None & False & \(n_{\text{HW}}=10\) \\ \hline \multirow{4}{*}{Steel plates} & EGS-MLP & NCL & None & None & False & \(r=1.0\), \(s_{\text{FWNO}}=\text{{\it adan}}\), \(n_{\text{HN}}=15\), \(\text{{\it acrf}}_{\text{HL}}=\text{{\it tanh}}\) \\  & EGS-kNN & None & None & None & False & \(r=1.0\), \(s_{\text{FWNO}}=\text{{\it adan}}\) \\ \cline{1-1}  & EGS-SVM & NCL & None & None & False & \(r=1.0\), \(\text{{\it c}}=4\) \\ \cline{1-1}  & EGS-RF & SSMOTE & None & None & False & \(r=1.0\), \(n_{\text{HW}}=10\) \\ \hline \multirow{4}{*}{SECOM} & EGS-MLP & RDUP & None & None & False & \(r=1.0\), \(s_{\text{FWNO}}=\text{{\it adan}}\), \(n_{\text{HN}}=20\), \(\text{{\it act}}_{\text{HL}}=\text{{\it relu}}\) \\ \cline{1-1}  & EGS-kNN & RDUP & None & None & False & \(r=1.0\), \(n_{\text{HN}}=2\), \(\text{{\it wf}}_{\text{HN}}=\text{{\it uniform}}\) \\ \cline{1-1}  & EGS-SVM & SSMOTE & None & None & False & \(r=1.0\), \(\text{{\it c}}=8\) \\ \cline{1-1}  & EGS-RF & SMOTE & None & None & False & \(r=1.0\), \(n_{\text{HW}}=8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best solutions found by the exhaustive grid-search approaches (Finally, we compare the running time between the approaches on a system with a four-core Intel(r) Core(tm) i7-6700 Processor 3.40 GHz and 16 GB of memory. As the execution time of the Kalman filter or voting ensemble is very small (less than 1 min), we compared the running time of DPGA to that of EGS-E only (Figure 6). Note that the running time is measured for the learning phase in Figure 1b. As shown in the figure, the running time of DPGA is even shorter than that of the EGS-E for the small-sized dataset (steel plates faults). For large datasets (FD001-FD004, and SECOM), the running time of DPGA approaches was, at most, 1.9 times longer than that of the EGS-E ones, which is practically acceptable considering the performance improvement.