# Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring
## 4 Experimental Results
Figure 3a is the testing curves of advanced ensemble methods for the Australian dataset; Figure 3b represents the testing curves of advanced ensemble algorithms for the German dataset; Figure 3c illustrates the testing curves of advanced ensemble approaches for the Japanese dataset; Figure 3d provides the testing curves of advanced ensemble methods for the Taiwan dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** & **BS** \\ \hline LR & 0.8549 & 0.9156 & 0.9175 & 0.8116 & 0.8613 & 0.103 \\ LDA & 0.8606 & 0.9127 & **0.9402** & 0.7997 & 0.8643 & 0.1136 \\ DT & 0.849 & 0.9134 & 0.8698 & 0.8561 & 0.8629 & 0.1123 \\ KNN & 0.8487 & 0.9111 & 0.8862 & 0.8345 & 0.8596 & 0.1107 \\ SVM & 0.8566 & 0.8682 & 0.9312 & 0.8008 & 0.8611 & 0.1175 \\ NN & 0.8487 & 0.9177 & 0.8895 & 0.831 & 0.8593 & 0.1048 \\ RF & 0.8548 & 0.9211 & 0.9293 & 0.7993 & 0.8594 & 0.1509 \\ GBDT & 0.8642 & 0.9362 & 0.8898 & **0.8625** & 0.8759 & 0.096 \\ AugBoost-ELM & **0.8687** & **0.9399** & 0.901 & 0.8582 & **0.8791** & **0.0942** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of credit scoring models for the Japanese dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** & **BS** \\ \hline LR & 0.6486 & 0.6999 & 0.6612 & 0.6099 & 0.6345 & 0.2179 \\ LDA & 0.6512 & 0.6985 & 0.6676 & 0.6023 & 0.6333 & 0.2183 \\ KNN & 0.6676 & 0.7169 & 0.7164 & 0.555 & 0.6254 & 0.2135 \\ SVM & 0.6731 & 0.7058 & **0.7561** & 0.5114 & 0.6101 & 0.214 \\ NN & 0.6806 & 0.7377 & 0.7131 & 0.6044 & 0.6543 & 0.2061 \\ DT & 0.6666 & 0.7199 & 0.6851 & 0.6167 & 0.6491 & 0.2152 \\ RF & 0.6949 & 0.7502 & 0.7298 & 0.6192 & 0.67 & 0.2011 \\ GBDT & 0.6948 & 0.7496 & 0.7297 & 0.6189 & 0.6698 & 0.2009 \\ AugBoost-ELM & **0.6963** & **0.7516** & 0.7322 & **0.6194** & **0.6711** & **0.2005** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of credit scoring models for the Taiwan dataset. As can be seen from Figure 3a, the converged testing AUC curve of AugBoost-ELM is close to that of AugBoost-NN, both of which are higher than the converged testing AUC of unsupervised AugBoost-based models such as AugBoost-RP and AugBoost-PCA. Moreover, as can be seen from Figure 3a, AugBoost-based models are superior to advanced ensemble approaches such as XGBoost and LightGBM. As can be observed from Figure 3b, AugBoost-ELM gets the highest converged testing AUC than other advanced ensemble approaches. Compared with unsupervised AugBoost-based models, XGBoost and LightGBM achieve better testing AUC while their converged testing AUC is worse than that of supervised AugBoost-based models; Compared with AugBoost-NN, AugBoost-ELM gets a slightly higher testing AUC than AugBoost-NN, demonstrating that AugBoost-ELM can be a good alternative to AugBoost-NN. As can be viewed from Figure 3c, similar to the results in Figure 3b, the testing AUC curve of AugBoost-ELM is slightly higher than that of another supervised enhanced GBDT model AugBoost-NN; AugBoost-based models get higher converged testing curves of advanced ensemble approaches. The same conclusion can be drawn from Figure 3d for the Taiwan dataset. Table 8 shows the performance comparison of advanced ensemble models for credit datasets. As can be seen from Table 8, on the Australian dataset, AugBoost-NN gets the best values of the metrics of AUC, recall, and F1 while AugBoost-PCA gets the optimal accuracy score and precision score. Compared with advanced ensemble approaches such as XGBoost, LightGBM, AugBoost-RP, and AugBoost-PCA, AugBoost-ELM achieves comparable performance on the AUC score, F1 score, demonstrating that AugBoost-ELM can be an alternative supervised AugBoost model to AugBoost-NN.