## 3 Results
In the case of KSC, the mean of ranking score was worse than that of GACV; however, its mean accuracy was better than that of the GACV and ranked after that of the DBTC because the KSC yielded a minor variation in accuracy according to the classification problem compared with the GACV. Computational time was measured to compare the computational complexity of each selection criterion (Table 4). This study utilized ten \(C\) and ten \(\gamma\) values equal to \(2^{-6}\), \(2^{-4}\),..., \(2^{10}\), and \(2^{12}\) in the MS. The time needed for evaluating the performance of SVM models on the grid points was obtained for each selection criterion with the use of MATLAB and a computer equipped with an Intel Core i7-6700 CPU and 24 GB RAM. The result will be changed according to the used MS method, the range of tested \(C\) and \(\gamma\) values, and the number of subjects and features, but the result will provide an insight on the computational complexity of selection criteria. In the simulation results, the time required for DBTC or ESDR was short. Other selection criteria test SVM models at all grid points, but the two selection criteria determined a \(\gamma\) value first by analyzing data, and a \(C\) value was selected. Therefore, the computational time was shorter than those of other selection criteria. In contrast, the time for the LSSC was the longest because it conducted cross validation \(N\) times for each grid point when the data of \(N\) subjects were used for MS. The time increased in the case of the classification problem, which contained many subjects, and the computational time of the LSSC was larger than others, particularly in problems 3-5, which contained 28 subjects. The computational time of the LSSC was also affected \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Problem** & **KSC** & **KSC2** & **DBTC** & **ESDR** & **XAB** & **GACV** & **LSSC** & **Optimal** \\ \hline
1 & 81.53 & 82.24 & 87.29 & 79.47 & 76.30 & 83.83 & 83.96 & 96.23 \\
2 & 74.00 & 73.97 & 72.97 & 73.42 & 71.19 & 64.19 & 79.33 & 89.22 \\
3 & 82.06 & 81.94 & 81.95 & 82.17 & 82.85 & 83.36 & 83.49 & 91.17 \\
4 & 88.90 & 88.85 & 89.10 & 89.02 & 90.42 & 90.67 & 90.51 & 96.57 \\
5 & 90.84 & 90.42 & 90.99 & 90.73 & 92.85 & 92.44 & 91.82 & 97.37 \\
6 & 72.80 & 72.80 & 72.95 & 72.74 & 70.22 & 50.65 & 73.41 & 76.21 \\
7 & 63.32 & 63.36 & 63.64 & 60.77 & 59.05 & 59.68 & 69.23 & 77.00 \\
8 & 64.05 & 64.00 & 64.55 & 61.77 & 59.91 & 65.00 & 70.73 & 79.50 \\
9 & 85.72 & 85.22 & 84.97 & 86.44 & 84.16 & 86.06 & 87.38 & 94.34 \\ \hline Mean & 78.13 & 78.09 & 78.71 & 77.39 & 76.33 & 75.10 & 81.09 & 88.62 \\ SD & 10.16 & 10.07 & 10.47 & 11.07 & 12.28 & 15.26 & 8.42 & 8.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (%) for each selection criterion. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problem** & **KSC** & **KSC2** & **DBTC** & **ESDR** & **XAB** & **GACV** & **LSSC** \\ \hline
8 & 4 & 5 & 3 & 6 & 7 & 2 & 1 \\
9 & 4 & 5 & 6 & 2 & 7 & 3 & 1 \\ \hline Mean & 4.22 & 4.89 & 3.67 & 4.78 & 5.22 & 3.67 & 1.44 \\ SD & 1.20 & 1.76 & 1.  
by the number of features. The number of subjects was the same in problems 3-5, but the computational time increased in the case of the problem that used more features. The comparison results on classification accuracy and computational time will provide valuable information when selecting a selection criterion for a classification problem on physiological data. For example, the LSSC had the highest classification accuracy but needed a long computational time. Therefore, it could be considered in a preferential manner in the common situation wherein MS is conducted offline. DBTC ranked second in terms of classification accuracy, but the computational time was short. It could be an alternative to the LSSC in situations where MS and the training process of SVM are implemented online.