## 7 Experiments and Results
### Effects of Fitness Function
The results are shown in Figure 9. The results are shown in Figure 10. The results are shown in Figure 11. The results are shown in Figure 12. The results are shown in Figure 13  
Using the dataset of Spanish companies, when comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with MSE as the fitness function, we noticed a major improvement in the g-mean from 0.211 to 0.842, an improvement in the F1 score from 0.104 to 0.141, and a drop in the accuracy from 0.978 to 0.749. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with accuracy as the fitness function, we observed similar results: a major increase in the g-mean from 0.131 to 0.842, an improvement in the F1 score from 0.054 to 0.141, and a drop in the accuracy from 0.979 to 0.749. Similarly, when comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with MSE as the fitness function, we noticed a major increase in the g-mean from 0.211 to 0.793, an improvement in the F1 score from 0.104 to 0.134, and a drop in the accuracy from 0.980 to 0.768. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with accuracy as the fitness function, we also observed a major increase in the g-mean from 0.062 to 0.793, an improvement in the F1 score from 0.032 to 0.134, and a drop in the accuracy from 0.980 to 0.768. We also observed similar results while using the dataset of Taiwanese companies. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with MSE as the fitness function, we noticed a major increase in the g-mean from 0.332 to 0.834, an improvement in the F1 score from 0.186 to 0.242, and a drop in the accuracy from 0.968 to 0.824. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with accuracy as the fitness function, we also noticed a major increase in the g-mean from 0.244 to 0.834, an increase in the F1 score from 0.110 to 0.242, and a drop in the accuracy from 0.967 to 0.824. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with MSE as the fitness function, the increase in the g-mean was from 0.290 to 0.845, the increase in the F1 score was from 0.147 to 0.237, and the drop in the accuracy was from 0.967 to 0.808. Likewise, when comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with accuracy as the fitness function, the increase in g-mean was from 0.207 to 0.845, the increase in the F1 score was from 0.087 to 0.237, and the drop in the accuracy was from 0.968 to 0.808. Moreover, we observed similar results while using the dataset of Polish companies. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with MSE as the fitness function, we noticed a major increase in the g-mean from 0.118 to 0.842, an improvement in the F1 score from 0.019 to 0.149, and a drop in the accuracy from 0.970 to 0.790. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with accuracy as the fitness function, we also noticed a similar increase in the g-mean from 0.118 to 0.842, an increase in the F1 score from 0.018 to 0.149, and a drop in the accuracy from 0.967 to 0.790. When comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with MSE as the fitness function, the increase in the g-mean was from 0.118 to 0.848, the increase in the F1 score was from 0.020 to 0.150, and the drop in the accuracy was from 0.970 to 0.790. Likewise, when comparing the ANN with the PSO and the cost sensitivity fitness function to the same classifier with accuracy as the fitness function, the increase in the g-mean was from 0.117 to 0.848, the increase in the F1 score was from 0.017 to 0.150, and the drop in the accuracy was from 0.967 to 0.790.