# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 5 Results and Discussion
### Performance Comparison with Baseline Classifiers and Recent Literature
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Algorithm** & **Sensitivity** & **Specificity** & **AUC** \\ \hline [PERSON] et al. [77] & Weighted ELM & 0.982 & - & 0.978 \\ [PERSON] et al. [78] & DNN & 0.955 & - & 0.990 \\ [PERSON] et al. [79] & CS-NNE & - & 0.936 & 0.980 \\ [PERSON] et al. [19] & GA-RF & 72.56 & - & 0.950 \\ [PERSON] et al. [80] & DT-NB & 0.872 & 1.000 & - \\ [PERSON] et al. [81] & Random forest-SMOTE & 0.829 & - & 0.910 \\ [PERSON] et al. [82] & Stochastic ensemble & 0.915 & - & 0.876 \\ [PERSON] et al. [83] & XGBoost-SMOTE & 0.988 & - & 0.970 \\ [PERSON] et al. [84] & GA-SVM & 0.963 & 0.963 & - \\ [PERSON] et al. [85] & PSO-based Ensemble model & 0.97 & - & - \\ [PERSON] et al. [86] & RHSO & 0.951 & - & - \\ [PERSON] et al. [87] & DRN-SSPO & 0.912 & 0.902 & - \\ This paper & Proposed IG-GAW & 0.997 & 0.994 & 0.990 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison with other well-performing methods in the literature. Meanwhile, the Taiwan dataset has 25 attributes and 30,000 samples, of which 23,364 are classified as good and 6636 as bad customers, i.e., 77.88% of the dataset belongs to the majority class and 22.12% belongs to the minority class. Therefore, the Taiwan dataset is also imbalanced. The features in the datasets and the selected feature sets are shown in Table 8. Meanwhile, Table 9 and Figure 5 show the performance of the various methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature-Selection Method** & \multicolumn{2}{c}{**Features**} \\ \hline Complete feature set & \begin{tabular}{c} Status of existing checking account, duration in month, credit history, purpose, credit amount, savings account, present employment since, installment rate as a percentage of disposable income, personal status and sex, other debtors, present residence since, property, age, other installment plans, housing, number of existing credits at this bank, job, number of dependents, telephone, foreign worker \\ \hline IG & \begin{tabular}{c} Status of existing checking account, duration in month, credit history, purpose, credit amount, savings account, present employment since, installment rate as a percentage of disposable income, personal status and sex, other debtors, property, age, other installment plans, housing, number of dependents, foreign worker \\ \hline GAW & \
\begin{tabular}{c} Status of existing checking account, duration in month, credit history, purpose, credit amount, savings account, present employment since, property, age, other installment plans, housing, number of dependents, foreign worker \\ \end{tabular} \\ \hline IG-GAW &
\begin{tabular}{c} Credit amount, status of existing checking account, duration in months, age, credit history, purpose, property, present employment since, and housing \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Feature sets from the German dataset. Figure 4: Comparative analysis of the various methods using the German dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Sensitivity** & **Specificity** & **AUC** & **G-Mean** \\ \hline AdaBoost & 0.785 & 0.892 & 0.810 & 0.837 \\ LR & 0.688 & 0.813 & 0.700 & 0.748 \\ RF & 0.796 & 0.904 & 0.830 & 0.850 \\ SVM & 0.649 & 0.792 & 0.650 & 0.716 \\ DT & 0.630 & 0.787 & 0.640 & 0.704 \\ ELM & 0.704 & 0.830 & 0.710 & 0.763 \\ IG-ELM & 0.796 & 0.903 & 0.810 & 0.847 \\ GAW & 0.820 & 0.925 & 0.860 & 0.871 \\ Proposed IG-GAW & 0.904 & 0.946 & 0.910 & 0.925 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison using the German dataset. Using the German dataset, the proposed IG-GAW achieved a sensitivity of 0.904, specificity of 0.945, and AUC of 0.910, as shown in Table 7. Meanwhile, the experimental results for the Taiwan dataset are shown in Table 9, and it shows that the proposed method obtained a sensitivity of 0.945, specificity of 0.961, and AUC of 0.940.