## 7 Experiments and Results
### Effects of Fitness Function  
We extended the MHOANN to add in the costs of misclassified instances during model training by implementing a cost sensitivity fitness function, which was based on the confusion matrix that was described in Section 5. For the problem in question, we tried to avoid FN predictions, i.e., the model predicts that a company is financially stable while it is actually in financial distress. Hence, we assigned a weighted cost to FN predictions. Determining the proper weight for the FN predictions depended on the dataset and the algorithm that were being used. We accomplished this by experimenting with different weights while monitoring the metrics to determine the best weight to use. Since the datasets in this work were relatively small in size, we were able to experiment using the whole datasets; however, in real applications with large datasets, we recommend using a sample of the dataset to find the best weight to use in order to reduce the computational costs. We considered the weight that yielded the highest g-mean score for the subsequent experiments. Table 2 shows the results for the dataset of Spanish companies with the PSO, Table 3 shows the results for the dataset of Spanish companies with the PSO, Table 4 shows the results for the dataset of Taiwanese companies with the PSO, Table 5 shows the results for the dataset of Taiwanese companies with the PSO, Table 6 shows the results for the dataset of Polish companies with the PSO, and Table 7 shows the results for the dataset of Polish companies with the PSO. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FN Weight** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **G-Mean** \\ \hline
1 & **0.967** & 0.064 & **0.997** & 0.110 & 0.244 \\
25 & 0.881 & 0.779 & 0.884 & **0.299** & 0.829 \\
50 & 0.824 & **0. From these experiments, we observed that the best weight for FN predictions when using the PSO for the dataset of Spanish companies was 100, as shown in Figure 6. The corresponding result was 75 when using the CSO for the same dataset, as shown in Figure 7. On the other hand, we noticed that the best weight for FN predictions when using the PSO for the dataset of Taiwanese companies was 50, as shown in Figure 8. The result was the same when the CSO was used for the same dataset, as shown in Figure 9. We also noticed that the best weight for FN predictions when using the PSO for the dataset of Polish companies was 175, as shown in Figure 10. The result was the same when using the CSO for the same dataset, as shown in Figure 11. After determining the best FN weight for each particular optimization algorithm and dataset, we trained the MHOANN using the cost sensitivity fitness function, fed it with the corresponding FN weight, and then used the trained model to classify the instances in the testing dataset. We observed that in order to obtain reasonable g-mean scores, the weight of the FN predictions needed to be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FN Weight** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **G-Mean** \\ \hline
1 & **0.967** & 0.014 & **0.987** & 0.018 & 0.118 \\
25 & 0.887 & 0.377 & 0.897 & 0.119 & 0.582 \\
50 & 0.824 & 0.4  
To assess the effects of the cost sensitivity fitness function, we based our results on a benchmark. In the benchmark, we used each optimizer (PSO and CSO) with two different fitness functions, MSE and accuracy, and then trained the ANN using both datasets to observe the evaluation metrics without cost-sensitive learning. For each dataset, we trained the ANN using the \(\chi^{2}\)-norm (\(\chi^{2}\)-norm) and \(\chi^{2}\)-norm (\(\chi^{2}\)-norm), respectively.