## 7 Experiments and Results
### Comparison to Other Approaches  
In [34], the authors proposed a hybrid method that combined the synthetic minority oversampling technique with other ensemble methods. Additionally, the authors applied five different feature selection methods to determine the most dominant attributes of insolvency prediction using the same dataset of Spanish companies. First, the authors compared four oversampling methods and then applied the C4.5 decision tree classifier to determine the best method. SMOTE was subsequently selected since it produced the \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **Optimizer** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **G-Mean** \\ \hline Cost-Sensitive & PSO & 0.824 & 0.848 & 0.823 & 0.242 & 0.834 \\ Ensemble Learning & PSO & 0.910 & 0.840 & 0.912 & 0.376 & 0.875 \\ \hline Percentage Change & & 10.4\% & \(-1.0\%\) & 10.8\% & 55.4\% & 4.9\% \\ \hline Cost-Sensitive & CSO & 0.808 & 0.888 & 0.806 & 0.237 & 0.845 \\ Ensemble Learning & CSO & 0.876 & 0.920 & 0.874 & 0.324 & 0.897 \\ \hline Percentage Change & & 8.4\% & 3.6\% & 8.4\% & 36.7\% & 6.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: A comparison between the results of the evaluation metrics from the cost-sensitive MHOANN and those from the cost-sensitive MHOANN within the majority voting ensemble learning system using the dataset of Taiwanese companies. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **Optimizer** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **G-Mean** \\ \hline Cost-Sensitive & PSO & 0.792 & 0.899 & 0.789 & 0.149 & 0.842 \\ Ensemble Learning & PSO & 0.898 & 0.913 & 0.898 & 0.261 & 0.905 \\ \hline Percentage Change & & 13.4\% & 1.6\% & 13.8\% & 75.2\% & 7.5\% \\ \hline Cost-Sensitive & CSO & 0.789 & 0.913 & 0.787 & 0.150 & 0.848 \\ Ensemble Learning & CSO & 0.888 & 0.928 & 0.887 & 0.269 & 0.907 \\ \hline Percentage Change & & 12.5\% & 1.6\% & 12.7\% & 79.3\% & 7.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: A comparison between the results of the evaluation metrics from the cost-sensitive MHOANN and those from the cost-sensitive MHOANN within the majority voting ensemble learning system using the dataset of Polish companies. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **Optimizer** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **G-Mean** \\ \hline Cost-Sensitive & PSO & 0.749 & 0.952 & 0.745 & 0.141 & 0.842 \\ Ensemble Learning & PSO & 0.851 & 0.905 & 0.850 & 0.207 & 0.877 \\ \hline Percentage Change & & 13.6\% & \(-5.0\%\) & 14.1\% & 46.8\% & 4.2\% \\ \hline Cost-Sensitive & CSO & 0.768 & 0.819 & 0.767 & 0.134 & 0.793 \\ Ensemble Learning & CSO & 0.883 & 0.905 & 0.882 & 0.251 & 0.893 \\ \hline Percentage Change & & 15.0\% & 10.5\% & 15.0\% & 87.3\% & 12.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: A comparison between the results of the evaluation metrics from the cost-sensitive MHOANN and those from the cost-sensitive MHOANN within the majority voting ensemble learning system using the dataset of Spanish companies. best results, as suggested by the authors. Second, the authors compared several standard basic and ensemble classification algorithms as the baseline for the study. Table 15 shows the g-mean scores when using the standard classifiers in [34] compared to those when using the two methods that are proposed in this work. It can be seen that the proposed methods produced higher g-mean scores than all of the other classifiers in the related study. Third, the authors compared several basic and ensemble classification algorithms after applying oversampling using SMOTE to compare their performances and select the best performing classifier. The AB-Rep tree was subsequently selected as the best classifier. Finally, the authors applied different attribute selectors for feature selection and then applied oversampling using SMOTE and classification using the AB-Rep tree algorithm before comparing the results.