## 3 Results  
Simulations were conducted to analyze the effectiveness of LSSC and compare it with other well-known selection criteria, namely KSC, KSC2, DBTC, ESDR, XAB, and GACV. These selection criteria were selected in this study because they have been extensively used for MS, and their effectiveness has been validated in several studies [25; 26; 27]. Among these, the KSC2 is a type of KSC and uses the mean value of the sensitivity and specificity rather than classification accuracy to evaluate a model. The KSC and KSC2 methods divided the entire data into five segments for cross validation. A simple grid-search method with C and \(\gamma\in\{2^{-6},\;2^{-4},...,\;2^{10},\;2^{12}\}\) was used as the MS method for all selection criteria. The performance of all models at the grid points was evaluated by the seven selection criteria, and the best model was selected for each selection criterion. [PERSON] was then trained using the selected model for each selection criterion, and the classification accuracies of the SVMs were compared. The LOSOCV was used to obtain the selection criterion for the MS in this study, but the LOSOCV is commonly used to test classifiers for the data of unseen subjects who were excluded from the training set, similar to real-world scenarios. Therefore, the aforementioned nine problems were validated and classification accuracies were obtained by using the LOSOCV in the simulations. Tables 2 and 3 show the simulation results for the classification accuracies and their rankings for the nine problems, respectively. The **Optimal** values are the results for the best cases with the highest grid-point accuracies in each test, and they are the upper limits of the classification accuracies that can be achieved by MS. The accuracy for the LSSC was 81.09% on average for the nine problems in the simulations. This value was lower than the **Optimal** value but outperformed other selection criteria. It was 2.38% higher than that of the DBTC, which yielded the second highest accuracy, and 2.96% higher than the KSC, which is the most commonly used selection criterion. The ranking of the LSSC was 1.44 on average, and it was superior to the rankings of the other selection criteria. The DBTC and GACV were the second-best selection criteria, but the variation in their rankings was severe. The standard deviation (SD) values of DBTC and GACV were 1.80 and 2.35 in the ranking, and they were larger than that of the LSSC, which had an SD value of 0.73. The ranking of the LSSC was not more than three and was equal to one in most cases. Therefore, the LSSC was more effective and less sensitive to the classification problem compared with the other selection criteria. Among the other selection criteria, the DBTC was the second most useful. The GACV had the same ranking on average with the DBTC, \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Problem** & **\#S** & **\#F** & **Data (Sample Size)** \\ \hline
1. Stress detection 1 & 10 & 22 & low (166) / medium + high stress (217) \\
2. Stress detection 2 & 18 & 20 & normal (1800)/stress (1800) \\
3. Stress detection 3 & 28 & 5 & normal (1692)/stress (1548) \\
4. Fatigue detection & 28 & 7 & normal (1692)/fatigue (3073) \\
5. Drowsiness detection & 28 & 8 & normal (1692)/drowsiness (3110) \\
6. Motor imaginary & 9 & 18 & left (1706)/right hand (1717) \\
7. Thermal comfort 1 & 11 & 29 & VH with cooler (1100)/VH (1100) \\
8. Thermal comfort 2 & 11 & 29 & VH with cooler (1100)/hot (1100) \\
9. VEB detection & 16 & 45 & normal (1600)/VEB (1600) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nine classification problems for MS simulations. #S and #F, respectively, denote the numbers of subjects and features. but it was less effective than some of the other selection criteria for some of the problems, such as problems 2 and 6. The DBTC was generally more stable and yielded smaller SD values compared with GACV in terms of classification accuracy and ranking, and the DBTC had a higher mean accuracy.