## 1 Introduction
The radius margin bound (RMB), maximal discrepancy (MD), xi-alpha bound (XAB), generalized approximate cross validation (GACV), distance between two classes (DBTC), and expected square distance ratio (ESDR) are some of the other selection criteria for MS [23; 24; 25]. The effectiveness of these selection criteria was compared in previous studies. [PERSON] used five datasets to analyze the benefits of seven different selection criteria, including the KCV-based selection criterion (KSC), XAB, and GACV; in their study, the test errors were calculated for the SVM models that used each of these selection criteria [26]. The KSC approach yielded the lowest error when used for MS, followed by the XAB and GACV approaches. [PERSON] simulated the MS for SVM using the KSC, XAB, GACV, DBTC, MD, XAB, and RMB approaches for 110 public datasets [27]. Similar to the results of [PERSON], KSC outperformed the other approaches in terms of classification accuracy, followed by the DBTC approach. The effectiveness of the selection criteria was verified for many datasets in previous studies, but the results may differ for physiological data which exhibit inter-subject variance (ISV). The physiological data are obtained from physiological signals such as electrocardiogram (ECG), electrodermal activity (EDA), and electromyogram (EMG). The signals are generated by the physical and mental activity of the human, and they can be used to know the information on our body. Responses to external stimuli and body conditions are not the same for every person, and the characteristics of physiological signals are different for each person [28]. The characteristic differences are reflected to physiological data as ISV, and it is one of factors that makes the analysis of the physiological data difficult. Owing to the ISV, the performance of a classifier can be degraded for the data of an unseen subject that are not included in the training set because there is a difference between the test and training data with respect to characteristics. Considering the difficulty, it is recommended to verify the performance of a classifier through leave-one-subject-out cross validation (LOSOCV) when physiological data are analyzed. The LOSOCV is similar to KCV but divides the entire data into datasets for each of the subjects for cross validation to know generalized performance for the data of an unseen subject. Indeed, [PERSON] recently showed that the LOSOCV is useful for evaluating the generalized ability of deep learning models for the data of unseen subjects, and it is necessary to tune the model based on LOSOCV [29]. These ISV and validation processes using LOSOCV must be considered in the MS process of SVM as well. Thus, results verified through a classification problem that contained training and test data with relatively similar characteristics should be re-examined for the MS on physiological data. [PERSON] compared several MS methods for medical datasets [30]. However, to our knowledge, to-this-date, no study has analyzed the effectiveness of selection criteria in MS cases associated with physiological data. Therefore, this study uses a LOSOCV-based selection criterion (LSSC) together with other well-known selection criteria, including KSC, DBTC, ESDR, XAB, and GACV for the MS of SVM on physiological data and compares their effectiveness. The LSSC reflects the principle of the validation process via LOSOCV to MS, and it evaluates the performance of an SVM model using the LOSOCV results of the data available for MS. Nine classification problems are defined using physiological data to simulate the MS of the SVM according to each of the selection criterion. Results show that the LSSC outperforms others in terms of classification accuracy. A detailed comparison of selection criteria for physiological data with ISV and a discussion on the case enhancing the advantages of LSSC are also primary contributions of this research.