Optimizing Feature Selection and Oversampling Using Metaheuristic Algorithms for Binary Fraud Detection Classification.

Identifying fraudulent transactions and preventing unauthorized individuals from revealing credit card information are essential tasks for different financial entities. Fraud detection systems are used to apply this task by identifying the fraudulent transactions from the normal ones. Usually, the data used for fraud detection is imbalanced, containing many more instances of normal transactions than fraudulent ones. This causes diminished classification task results because it is hard to train a classifier that distinguishes between them. Another problem is caused by many features under study for the fraud detection task. This paper utilizes different metaheuristic algorithms for feature selection to solve the problem of unneeded features and uses the Synthetic Minority Oversampling TEchnique (SMOTE) to solve the imbalance problem of the data using different classification algorithms. The metaheuristic algorithms include Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and A Multi-Verse Optimizer (MVO), whereas the classification algorithms include Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The results show that applying the oversampling technique generated better results for the G-Mean and Recall values, while the feature selection process enhanced the results of almost all the classification algorithms.