# 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  
In this section, the proposed hybrid approach is benchmarked with other ML classifiers and methods in the literature. The baseline classifiers were trained with the complete feature set. The performance of the classifiers is tabulated in Table 4 and visualized in Figure 3. The models obtained relatively high specificity compared to the sensitivity, which implies the model correctly predicted more non-fraud transactions (majority class) than fraud transactions (minority class). Meanwhile, in credit card fraud detection, like every imbalance classification task, it is more important to predict the minority class samples correctly. However, the proposed method achieved excellent sensitivity and specificity, indicating its robustness in predicting the minority and majority class samples. This enhanced performance could be attributed to using the G-mean as the fitness function rather than the widely used accuracy criterion, ensuring the model's detection rate on the minority class is enhanced. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Sensitivity** & **Specificity** & **AUC** & **G-Mean** \\ \hline AdaBoost & 0.889 & 0.918 & 0.900 & 0.903 \\ LR & 0.752 & 0.916 & 0.810 & 0.829 \\ RF & 0.869 & 0.940 & 0.890 & 0.904 \\ SVM & 0.585 & 0.827 & 0.660 & 0.695 \\ DT & 0.590 & 0.801 & 0.690 & 0.688 \\ Proposed IG-GAW & 0.997 & 0.994 & 0.990 & 0.994 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with other baseline classifiers. Figure 2: ROC curve of the ELM classifier, filter, wrapper, and hybrid feature-selection methods. Furthermore, Table 5 shows the performance of some state-of-the-art methods in the literature. The methods include a weighted extreme learning machine (weighted ELM) [77], a deep neural network (DNN)-based classifier [78], a cost-sensitive neural network ensemble (CS-NNE) [79], a random forest-based genetic algorithm wrapper method (GA-RF) [19], a method that sequentially combines C4.5 and the naive Bayes model (DT-NB) [80], a model developed using the random forest algorithm with the SMOTE technique (RF-SMOTE) [81], a stochastic ensemble model [82], an XGBoost-SMOTE model [83], a support vector machine (SVM)-based GA wrapper [84], an ensemble model optimized using the particle swarm optimization (PSO) technique [85], a metaheuristic based feature selection algorithm based on rock hyrax swarm optimization (RHSO) [86], and a deep residual network with shuffled shepherd optimization (DRN-SSPO) [87]. Table 5 shows that the proposed IG-GAW demonstrated excellent results compared to the state-of-the-art methods, indicating the proposed method's robustness. Furthermore, the proposed method is applied to other credit card datasets to show how the method performs in different scenarios. The datasets include the German credit card dataset [88] and the Taiwan credit card dataset [89]. The German dataset has 20 attributes and contains 1000 samples, of which 700 are classified as good and 300 as bad customers, i.e., 70% of the dataset belongs to the majority class, while 30% belongs to the minority class; hence, the dataset is imbalanced. The features in the German dataset and those selected by the various feature selection techniques are shown in Table 6, while Table 7 and Figure 4 show the performance of the various models. Figure 3: Comparative analysis of the various methods using the credit card dataset.