## 3 Computational Results
### Experimental Results
Since the performance measures (MAE, MAPE, and RMSE) are about the prediction errors, the best prediction results were obtained are shaded grey in Table 2 when \(G=20\), \(\delta=0.01\), and \(\xi=7\). Furthermore, to evaluate the effectiveness and efficiency of the proposed hybrid approach, we compare with five existing approaches that were implemented for the same dataset as ours in previous works ([PERSON] 2007b, [PERSON] 2016b); they are (i) linear regression (LR), (ii) BPN, (iii) \(k\)-means BPN, (iv) \(k\)-means fuzzy BPN (36), and (v) post-classification BPN ([PERSON] [58]). In Table 3, the best prediction results obtained with the proposed approach with \(G=20\), \(\delta=0.01\), and \(\xi=7\) are compared with those of the five existing approaches. The best results in the previous research were obtained by post-classification BPN ([PERSON] 2016b). The comparison for MAE was made through the following equation. For MAPE and RMSE, similar calculations have been performed to calculate the improvements over the best-known methods. \[\text{Improvement}\left(\%\right)=\frac{\text{MAE}(\text{best known method})-\text{MAE}(\text{proposed method})}{\text{MAE}(\text{best known method})}, \tag{18}\]  
As shown in Table 3, the prediction accuracies are improved by 16.3%, 4.6%, and 19.6% as measured in terms of MAE, MAPE, and RMSE, respectively. Comparing the proposed approach with the previous ones in Table 3 suggests that the former is effective and applicable. It is also clear that the classification-based hybrid approaches (k-means BPN, k-means fuzzy BPN, post-classification BPN, and the proposed approach) perform better than the other approaches (LR and BPN). The proposed hybrid approach is better than other BPNs because it is challenging to train the BPN using raw job records because of the overfitting problem. The proposed approach is better than the k-means fuzzy BPN approach because a better classification is obtained using the FCM-based GA method. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**G**.} & \multirow{2}{*}{\(\xi\)} & \multicolumn{8}{c}{**Best Value**} & \multicolumn{8}{c}{**Average Value**} \\ \cline{3-10}  & & & \(\delta\) & **0.01** & **0.05** & **0.08** & **0.1** & **0.01** & **0.05** & **0.08** & **0.1** \\ \hline \multirow{8}{*}{22} & \multirow{8}{*}{7} & MAE (h) & 123.99 & 116.03 & 144.75 & 96.68 & 144.20 & 190.13 & 187.16 & 156.08 \\  & & MAPE (\%) & 10.90 & 10.00 & 12.20 & 8.50 & 12.60 & 16.70 & 16.30 & 13.20 \\  & & RMSE (h) & 140.28 & 147.88 & 182.79 & 117.55 & 181.68 & 238.56 & 232.60 & 186.85 \\  & & MAE (h) & 122.03 & 128.79 & 152.29 & 118.62 & 166.13 & 157.95 & 179.16 & 160.69 \\  & & MAPE (\%) & 10.40 & 11.00 & 13.00 & 9.90 & 14.40 & 13.70 & / & 13.70 \\  & & RMSE (h) & 171.08 & 155.78 & 187.71 & 138.90 & 198.50 & 194.35 & 222.87 & 194.79 \\  & & MAE (h) & 128.49 & 111.02 & 107.62 & 87.14 & 145.13 & 141.60 & 149.09 & 142.26 \\  & & MAPE (\%) & 11.00 & 9.30 & 8.90 & 7.60 & 12.40 & 12.00 & 13.10 & 12.20 \\  & & RMSE (h) & 159.06 & 142.54 & 150.50 & 133.37 & 177.79 & 167.46 & 191.49 & 180.90 \\  & & MAE (h) & 131.87 & 107.62 & 143.40 & 90.92 & 148.16 & 156.34 & 158.13 & 153.17 \\  & & MAPE (\%) & 11.40 & 9.20 & 12.70 & 8.00 & 12.70 & 13.40 & 13.50 & 13.30 \\  & & RMSE (h) & 170.71 & 136.10 & 159.68 & 128.74 & 185.41 & 187.92 & 190.67 & 185.97 \\  & & MAE (h) & 123.17 & 97.93 & 124.83 & 106.08 & 144.37 & 133.21 & 153.99 & 152.81 \\  & & MAPE (\%) & 10.70 & 8.30 & 10.50 & 8.80 & 12.40 & 11.60 & 13.20 & 12.90 \\  & & RMSE (h) & 149.38 & 139.45 & 161.30 & 155.14 & 175.48 & 163.15 & 190.40 & 194.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)