## 3 Results and Discussion
### Results Corresponding to All the Datasets
It also shows the average ranks computed using all the methods in the experimental setup. In terms of average accuracies, the best results obtained by [PERSON] et al. (2018) appear in the lower third of the table; SVM-LOB (Linear SVM trained using the concatenation of Raw and Basic features) achieves an average accuracy of 91.36%. The same classifier, when trained using all features or TSFRESH with feature selection, obtains an accuracy higher than 94.5%. The best five pairs according to the average accuracy and rank (in Table 4) use the feature sets (t), (All), and (Ot). Table 5 summarizes the results in Table 4 averaging for each feature set the corresponding values of RF, SVM-L and SVM-G. According to both the average accuracies and ranks, the three best feature sets are (t), (All), and (Ot). Figure 4 shows, for each one of the three classification methods, the differences in terms of accuracy between each feature set and the feature set (OB) used by [PERSON] et al. [18]. Each boxplot is from the corresponding differences from the 34 datasets. The average differences are clearly favorable for several of the alternative feature sets. Medians of the differences are close to 0 or negative. As shown in Tables A1-A4, there are several datasets with 100% accuracy for all or many of the feature sets. For several feature sets, the boxplot are mostly in the positive region, positive differences are greater than negative differences. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Accuracy** & **Method** & **Rank** \\ \hline SVM-G:OR100 & 93.252 & SVM-G:OR1000 & 17.588 \\ SVM-L:T & 93.205 & SVM-G:Ot & 17.662 \\ SVM-G:OR1000 & 93.070 & RF:t & 17.794 \\ SVM-G:R1000 & 92.969 & SVM-G:t & 17.853 \\ RF:t & 92.926 & SVM-L:OB & 18.132 \\ SVM-L:R1000 & 92.840 & RF:All & 18.176 \\ RF:All & 92.720 & SVM-G:R1000 & 18.265 \\ RF:Ot & 92.479 & RF:Ot & 18.353 \\ SVM-G:OB & 92.375 & RF:T & 19.279 \\ SVM-L:OR100 & 92.141 & SVM-L:O & 19.603 \\ RF:OT & 92.100 & SVM-L:OT & 20.176 \\ RF:T & 92.028 & RF:OT & 20.426 \\ SVM-G:O & 91.987 & SVM-L:R100 & 21.044 \\ SVM-L:OB & 91.355 & SVM-G:OT & 21.103 \\ SVM-G:R100 & 90.877 & SVM-G:T & 21.382 \\ SVM-L:R100 & 90.712 & SVM-L:T & 21.544 \\ RF:OR1000 & 90.706 & RF:OR1000 & 21.779 \\ RF:OR100 & 90.110 & SVM-G:R100 & 22.765 \\ SVM-G:B & 90.100 & RF:OR100 & 22.897 \\ SVM-L:O & 89.989 & RF:OB & 23.176 \\ RF:OB & 89.823 & RF:R1000 & 23.338 \\ RF:R100 & 89.761 & RF:O & 24.706 \\ RF:O & 89.333 & RF:R100 & 25.971 \\ RF:R100 & 88.747 & SVM-G:B & 26.603 \\ RF:B & 87.508 & SVM-L:B & 28.088 \\ SVM-L:B & 87.068 & RF:B & 28.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\) \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature Set** & **Accuracy** & **Feature Set** & **Rank** \\ \hline t & 94.024 & All & 16.039 \\ All & 93.811 & Ot & 16.490 \\ Ot & 93.628 & t & 16.833 \\ OT & 92.907 & OR1000 & 18.245 \\ T & 92.864 & OR100 & 18.436 \\ OR1000 & 92.353 & OB & 19.495 \\ R1000 & 91.857 & R1000 & 19.647 \\ OR100 & 91.834 & O & 20.377 \\ OB & 91.184 & OT & 20.569 \\ O & 90.437 & T & 20.735 \\ R100 & 90.112 & R100 & 23.260 \\ B & 88.226 & B & 27.892 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracies and ranks for each feature set, from all the datasets. For each feature set, the values in these tables are the averages for RF, SVM-L, and SVM-G in Table 4.  
For SVM-G the boxplots are less favorable to the alternative feature sets, but, as shown in Table 4, SVM-L has better results than SVM-G. Given the large number of (Classifier:Feature Set) tested methods, it is preferable to obtain the average ranks by considering smaller pair groups. Therefore, methods were divided depending on the type of classifier used: RF, SVM-L, and SVM-G. These average ranks are shown in Table 6.