In each cycle, the samples in the testing set are included into the current training set.
Table 1 Datasets' characteristics Dataset # samples (Questionnaires) # samples in the training set # samples in the test set # parties modelled Cypriot 1,897 1,138 759 7 German 5,180 3,108 2,072 7 Greek 26,243 15,746 10,497 9.
The MDs between those samples in the testing set and these three populations were calculated, as shown in Table 4.
As previously mentioned in the performance assessment analysis, the samples in the testing set (numbered as F1_1 to F1_4 and F2_1 to F2_4) were detected as fault states.
To identify which type of fault they belonged to, as a reference, the normal samples in the testing set (numbered as N_1 to N_4) were also considered.
To identify which type of fault they belonged to, as a reference, the normal samples in the testing set (numbered as N_T1 to N_T4) were also considered, the MDs between the samples in the testing set and those four populations were calculated, as shown in Table 8.
None of the samples in the testing panel overlapped with the ascertainment samples.
This computation is performed within the data of sample S and does not use data of other samples in the test dataset.
After clustering the training set, this information was used for predictive classification of the samples in the testing set (Table 2).
Finally, Chen et al. [52] perfectly classifies all samples in the test set using 10 genes with SVM and kernel Fisher discriminant analysis.
