## 4 Results
### Classification Using Hold-Out Validation  
For the first group of experiments, 10 classifiers and their default parameters were implemented in a hold-out validation strategy. These experiments aimed to find the most suitable classifier for the task. In addition, different embedding sizes or Doc2 Vec (from now on, D2V) output dimensions were analyzed. The results corresponding to D2V output dimensions fixed at 50, 100, 300, and 500 with the 10 classifiers are concentrated in Tables 4-7. These tables highlight the best results for each case and each  
From the reported evaluation metrics, it can be concluded that the most suitable classifier for the task is the support vector classifier with a radial basis kernel and a balanced approach (from now on, RBF SVC-balanced). A summary of the best results using this classifier for the hold-out validation strategy is shown in Table 8, where the best results for each computed metric are highlighted. Table 8 shows that the most suitable D2V output dimension is 300, which, in terms of the classifier input dimension, is 600. For reference, the input of the classifiers is double the D2V output since it is the concatenation of the inferred vectors of each selected channel (Figure 2 4). Finally, Figure 6 exhibits the normalized confusion matrix obtained using the RBF SVC--balanced classifier with an input dimension of 600; that is, a D2V output dimension of 300. This classifier has no further problem classifying the classes CAP1 and CAP3, whereas class CAP2 is easily misclassified as CAP1. \begin{table}
\begin{tabular}{l c c c c} \hline
**Classifier** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\ \hline
3-NN & 0.6763 & 0.6185 & 0.6763 & 0.6469 \\ Linear SVC & 0.6380 & 0.6648 & 0.6380 & 0.7177 \\ RBF SVC & **0.7688** & 0.7314 & **0.7688** & 0.7358 \\ RBF SVC—balanced & 0.7595 & **0.7651** & 0.7595 & **0.7720** \\ Decision Tree & 0.6265 & 0.6520 & 0.6265 & 0.6931 \\ Random Forest & 0.6845 & 0.6738 & 0.6845 & 0.6652 \\ MLP & 0.7535 & 0.7192 & 0.7535 & 0.7132 \\ AdaBoost & 0.7437 & 0.7066 & 0.7437 & 0.6973 \\ Naive Bayes & 0.71084 & 0.7255 & 0.7108 & 0.7551 \\ XGBoost & 0.7365 & 0.6669 & 0.7365 & 0.6481 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification results with hold-out validation (D2V output dimension = 300). \begin{table}
\begin{tabular}{l c c c c} \hline
**D2V** & **Clf.** & & \\
**Output** & **Input** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\
**Dimension** & **Dimension** & & & & \\ \hline
50 & 100 & 0.7568 & 0.7632 & 0.7568 & 0.7713 \\
100 & 200 & 0.7590 & 0.7649 & 0.7590 & **0.7724** \\
300 & 600 & **0.7595** & **0.7651** & **0.7595** & 0.7720 \\
500 & 1000 & 0.7469 & 0.7543 & 0.7469 & 0.7638 \\ \hline \end{tabular}
\end{table} 
Table 8: Best classification results with hold-out validation, obtained with RBF SVC—balanced. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Classifier** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\ \hline
3-NN & 0.6796 & 0.6219 & 0.6796 & 0.6717 \\ Linear SVC & 0.6297 & 0.6584 & 0.6297 & 0.7170 \\ RBF SVC & **0.7645** & 0.7271 & **0.7645** & 0.7257 \\ RBF SVC—balanced & 0.74698 & **0.7543** & 0.7469 & **0.7638** \\ Decision Tree & 0.6495 & 0.6660 & 0.6495 & 0.6899 \\ Random Forest & 0.6998 & 0.6947 & 0.6998 & 0.6925 \\ MLP & 0.7398 & 0.7240 & 0.7398 & 0.7142 \\ AdaBoost & 0.7437 & 0.7108 & 0.7437 & 0.7063 \\ Naive Bayes & 0.7108 & 0.7269 & 0.7108 & 0.7601 \\ XGBoost & 0.7420 & 0.6704 & 0.7420 & 0.7041 \\ \hline \end{tabular}
\end{table} 
Table 7: Classification results with hold-out validation (D2V output dimension = 500). As shown in the first group of results, Tables 9-12 highlight the best results for each case and each computed metric, although the F1 score is the most relevant for unbalanced tasks like this one.