# Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring
## 4 Experimental Results
Based on the good advantage of boosting framework, AugBoost-ELM, which is stage-wisely enhanced by the ELM-based supervised feature augmentation mechanism for the boosting framework, gets better predictive performance. Table 6 is the performance comparison of credit scoring models for the Japanese dataset. As is shown in Table 6, AugBoost-ELM achieves optimal accuracy score, AUC score, F1 score, and BS while LDA gets the best precision score and GBDT gets the optimal recall score. LDA gets a high precision score and low recall score, suggesting that LDA is a good \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** & **BS** \\ \hline LR & **0.8649** & 0.9298 & 0.8309 & 0.8741 & 0.852 & 0.0992 \\ LDA & 0.8594 & 0.9269 & 0.7961 & **0.9196** & **0.8534** & 0.1089 \\ DT & 0.8437 & 0.914 & 0.827 & 0.8202 & 0.8236 & 0.1113 \\ KNN & 0.8494 & 0.9134 & **0.864** & 0.7851 & 0.8227 & 0.1112 \\ SVM & 0.8626 & 0.9262 & 0.8497 & 0.8395 & 0.8446 & 0.1008 \\ NN & 0.8502 & 0.9148 & 0.8328 & 0.8298 & 0.8313 & 0.1186 \\ RF & 0.8645 & 0.9338 & 0.8575 & 0.8341 & 0.8457 & 0.1032 \\ GBDT & 0.8637 & 0.9392 & 0.8426 & 0.853 & 0.8478 & **0.0956** \\ AugBoost-ELM & 0.8635 & **0.9422** & 0.8485 & 0.8439 & 0.8462 & 0.0993 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of credit scoring models for the Australian dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** & **BS** \\ \hline LR & 0.7601 & 0.7808 & 0.7942 & 0.8872 & 0.8381 & 0.1646 \\ LDA & 0.7585 & 0.7795 & 0.7926 & 0.8871 & 0.8372 & 0.1653 \\ DT & 0.7232 & 0.7096 & 0.7791 & 0.8439 & 0.8102 & 0.1928 \\ KNN & 0.728 & 0.7384 & 0.7341 & 0.9586 & 0.8315 & 0.1803 \\ SVM & 0.7065 & 0.7112 & 0.7966 & 0.7798 & 0.7881 & 0.1859 \\ NN & **0.7659** & 0.7799 & **0.8067** & 0.8753 & 0.8396 & 0.165 \\ RF & 0.747 & 0.7733 & 0.7571 & **0.9407** & 0.839 & 0.17 \\ GBDT & 0.7586 & 0.7786 & 0.788 & 0.8962 & 0.8386 & 0.1655 \\ AugBoost-ELM & 0.7617 & **0.7861** & 0.7775 & 0.9245 & **0.8446** & **0.1637** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of credit scoring models for the German dataset. method to discriminate the good applicant despite the ability to predict the bad applicants is poor. Moreover, compared with statistical-based algorithms and ML-based individual classifiers, the leading performance of RF, GBDT, and AugBoost-ELM further shows that ensemble strategy is practicable for the performance improvement for credit scoring. Table 7 provides the performance comparison of credit scoring models for the Taiwan dataset. As can be seen from Table 7, AugBoost-ELM realizes optimal scores on the metrics of accuracy, AUC, recall, F1, and BS, the effectiveness of AugBoost-ELM is fully illustrated. The improvement of recall score of AugBoost-ELM specifies the results that AugBoost-ELM improves the performance of credit scoring by reducing the misclassification of \"bad\" applicants. Moreover, as can be seen from Table 7, ML-based individual classifiers outperform statistical-based algorithms on the Taiwan dataset. Though RF gets a high precision score, its recall score poor ability on discriminating \"bad\" applicants results in the poor performance of credit scoring for the Taiwan dataset. Besides, the leading performance of AugBoost-ELM compared with GBDT further demonstrates ELM-based supervised features augmentation can be a candidate for the improvement of boosting framework. To further verify the effectiveness of AugBoost-ELM, we further select five advanced ensemble credit scoring models for comparison, which includes XGBoost, LightGBM, AugBoost-RP, AugBoost-PCA, AugBoost-NN while AugBoost-RP is step-wisely augmented by random projection method, AugBoost-PCA is step-wisely enhanced by principal component analysis (PCA), and AugBoost-NN is enhanced by NN algorithm. Figure 3 shows the testing AUC curves of advanced ensemble credit scoring algorithms.