# Recognition of Cross-Language Acoustic Emotional Valence Using Stacked Ensemble Learning
## 4 Results and Discussion
The same pattern of performance was observed in the precision, recall, and F1-score of the ensemble algorithms. The superiority of the proposed RALOG algorithm was also observed when precision was analyzed in Table 6. The impact of using the RF-RFE algorithm to scale features was again noticed when the precision was evaluated. The average precision for all the learning algorithms increased considerably. GBM experienced the highest increment from 69% to 79%, while the average precision of RALOG increased from 82% to 90%. The RALOG algorithm achieved the highest average precision of 90% followed by the RDF algorithm (80%). The RDF algorithm was closely followed by the GBM algorithm that achieved an average precision score of 79%. In this regard, the GBM was followed by the ETC and XGB algorithms that achieved similar average precision scores of 78% each. It can be observed from the results in Table 6 that RALOG is a good learning algorithm for recognizing valence emotion in speech files, and the results are comparable to those of the work presented in ([PERSON] et al., 2019). The average recall as illustrated in Table 6 was improved marginally when feature selection was applied. We observed that RALOG outperformed the other learning algorithms in this regard because it obtained an average recall score of 90%. RALOG was followed by RDF (88%), XGB (85%), GBM (82%), and ETC (82%). Although GBM achieved the lowest average recall score, it had the highest percentage increment of 11% after feature scaling. These results present a further confirmation that strongly underlines the fact that the application of RALOG with the RF-RFE algorithm for valence emotion recognition is indeed promising. The average recall of RALOG is relatively high when compared to the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Stage** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
**RALOG** & **Before RFE** & 82 (\(\pm\)0.007) & 83 (\(\pm\)0.007) & 83 (\(\pm\)0.007) & 82 (\(\pm\)0.007) \\  & **After RFE** & 90 (\(\pm\)0.006) & 90 (\(\pm\)0.006) & 91 (\(\pm\)0.006) & 92 (\(\pm\)0.005) \\ \hline
**RDF** & **Before RFE** & 72 (\(\pm\)0.009) & 82 (\(\pm\)0.007) & 77 (\(\pm\)0.008) & 80 (\(\pm\)0.008) \\  & **After RFE** & 80 (\(\pm\)0.008) & 88 (\(\pm\)0.006) & 82 (\(\pm\)0.007) & 85 (\(\pm\)0.007) \\ \hline
**GBM** & **Before RFE** & 69 (\(\pm\)0.009) & 73 (\(\pm\)0.009) & 71 (\(\pm\)0.009) & 73 (\(\pm\)0.009) \\  & **After RFE** & 79 (\(\pm\)0.008) & 82 (\(\pm\)0.007) & 80 (\(\pm\)0.008) & 85 (\(\pm\)0.007) \\ \hline
**XGB** & **Before RFE** & 71 (\(\pm\)0.009) & 81 (\(\pm\)0.008) & 75 (\(\pm\)0.008) & 78 (\(\pm\)0.008) \\  & **After RFE** & 78 (\(\pm\)0.008) & 85 (\(\pm\)0.007) & 81 (\(\pm\)0.006) & 87 (\(\pm\)0.007) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage average precision, recall, F1-score, and accuracy with confidence intervals of learning algorithms at each experimental stage of feature scaling with the RF-RFE algorithm. results in the literature [39]. In addition, RALOG achieved the highest average F1-score of 91% followed by RDF (82%) and XGB (81%). ETC and GBM both achieved the lowest average F1-score of 80%. All the learning algorithms increased their F1-scores for each respective class when the RF-RFE algorithm was applied. Furthermore, ETC and GBM achieved the highest average F1-score increment of 9%. Moreover, the results confirm that the performance of RALOG is comparable to the proposal presented in the literature [56]. Table 7 shows the results of percentage precision, recall, F1-score, and accuracy for valence instances. The results show that ensemble algorithms achieved lower scores in recognizing instances of valence emotion. This could be ascribed to the unbalanced structure of the cross-language corpus. There was a significant increase in performance across all performance metrics when the RF-RFE algorithm was applied. Specifically, RDF achieved the highest precision score of 97% in recognizing negative valence emotion instances followed by XGB (95%).