Credit Card Fraud Detection: Comparing Dimensionality Elevation and Reduction, Single Model and Stacking Model

With the rapid progression of high technology, new-style fraud has emerged in the past decade, among which credit card fraud has been exceptionally grave. Hence, the credit card fraud detection is of vital importance in preventing financial loss, establishing consumer protection and maintaining trust in digital transactions. Previous studies tend to implement dimensionality reduction methods like Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to preprocess the data. Additionally, various modelsincluding Logistic Regression, Nave Bayes, Decision Tree, and Random Forestwere independently employed to assess their individual performance in prior studies. The results of each model were then systematically compared against one another to identify the top-performing algorithm. There are, however, primarily two research gaps. Few research have used dimensionality elevation procedures, despite the fact that dimensionality reduction approaches are frequently used in preprocessing processes. Furthermore, the stacking approach has been proven to be successful in other domains, such as virus diagnosis, but it has been utilized infrequently in credit card fraud detection. Therefore, in this study, the dimensionality elevation method PolynomialFeatures is utilized to compare with the dimensionality reduction method t-SNE and a stacking model is implemented to explore whether it will have a higher accuracy compared to a single model. This study chooses Logistic Regression as the stacking model due to its simple linear form, making it less prone to overfitting compared to complex meta-models like Random Forests or Deep Neural Networks.