# Automatic Age and Gender Recognition Using Ensemble Learning
## 5 Discussion  
This article used a classification approach based on ensemble learning to recognize the speaker's age and gender classes. First, five models that classify speakers according to their age and gender were created using five different machine learning methods, and the performance of each was analyzed. When the results in Table 3, where the performance measurements obtained for these models are given, are examined, SVM, E-TREE, and RF classifiers offer over 95% accuracy, while the accuracy of KNN and LDA classifiers remains around 91%. This difference may be due to the fact that KNN is a type of instance-based learning and does not create a general internal model, while LDA classifies based on a linear combination of features. Then, eight ensemble models were developed in which the results of these classifiers were combined with majority voting and stacking approaches, and the performance of these models was evaluated. In tests performed on the same dataset and in \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Base-Level Classifiers** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SVM + E-TREE & 96.87 & 96.90 & 96.87 & 96.86 \\ SVM + E-TREE + RF & 97.16 & 97.20 & 97.16 & 97.17 \\ SVM + E-TREE + RF + KNN & 96.87 & 96.90 & 96.87 & 96.86 \\
**SVM + E-TREE + RF + KNN + LR** & **97.41** & **97.44** & **97.41** & **97.41** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of the stacking-based ensemble models. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline  & **F80s** & **F50s** & **M50s** & **F40s** & **M40s** & **F70s** & **F60s** & **M60s** & **F30s** & **M30s** & **F20s** & **M20s** \\ \hline F80s & **100** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ F50s & 0 & **92.66** & 0 & 0 & 0 & 3.95 & 0 & 0 & 1.13 & 0 & 2.26 & 0 \\ M50s & 0 & 0 & **100** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ F40s & 0 & 0 & 0 & **98.06** & 0 & 0.65 & 0 & 0.65 & 0 & 0 & 0.65 & 0 \\ M40s & 0 & 0 & 0 & 0.66 & **94.70** & 0.66 & 0 & 1.32 & 0 & 1.32 & 0 & 1.32 \\ F70s & 0 & 0.64 & 0 & 0 & 0 & **99.36** & 0 & 0 & 0 & 0 & 0 & 0 \\ F60s & 0 & 0 & 0 & 0.61 & 0 & 0 & **98.17** & 0 & 0 & 0 & 1.22 & 0 \\ M60s & 0.65 & 0 & 0 & 0 & 0.65 & 0 & **98.71** & 0 & 0 & 0 & 0 \\ F30s & 0 & 0 & 0 & 1.10 & 0 & 0 & 0 & 0 & **97.24** & 0 & 1.66 & 0 \\ M30s & 0.60 & 0 & 0 & 0.60 & 0 & 0 & 0 & 0 & **98.81** & 0 & 0 \\ F20s & 0.55 & 2.19 & 0 & 0.55 & 1.09 & 2.19 & 0 & 0 & 1.09 & 0 & **92.35** & 0 \\ M20s & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.60 & 0 & 0 & 0 & **99.40** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of the model generated by stacking E-TREE, KNN, SVM and RF. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Base-Level Classifiers** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SVM + E-TREE & 96.27 & 96.44 & 96.27 & 96.21 \\ SVM + E-TREE + RF & 96.27 & 96.43 & 96.27 & 96.24 \\
**SVM + E-TREE + RF + KNN** & **96.72** & **96.84** & **96.72** & **96.68** \\ SVM + E-TREE + RF + KNN + LR & 96.52 & 96.64 & 96.52 & 96.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of the majority voting-based ensemble models. the same experimental setup, it is seen that both ensemble approaches provide an increase in age and gender classification performance. Additionally, the experimental results reveal that models developed with the stacking approach provide superior results compared to those developed with majority voting. This shows that the stacking approach takes better advantage of the complementary aspects of the classifiers that make up the ensemble. In the study, the ensemble model created by stacking five base-level classifiers is the model with the highest accuracy with an accuracy rate of 97.41%. The study presents a comparison of the performance of the model based on the ensemble approach with state-of-the-art methods in Table 8. Upon reviewing the table, it becomes apparent that there are differences in the class definitions used in the various studies.