Analysis of Credit Card Fraud Detection Using Fusion Classifiers

Credit card fraud detection is a critical problem that has been faced by online vendors at the finance marketplace every now and then. The rapid and fast growth of the modern technologies causes the fraud and heavy financial losses for many financial sectors. Different data mining and soft computing-based classification algorithms have been used by most of the researchers, and it plays an essential role in fraud detection. In this paper, we have analyzed some ensemble classifiers such as Bagging, Random Forest, Classification via Regression, Voting and compared them with some effective single classifiers like K-NN, Naive Bayes, SVM, RBF Classifier, MLP, Decision Tree. The evaluation of these algorithms is carried out through three different datasets and treated with SMOTE, to deal with the class imbalance problem. The comparison is based on some evaluation metrics like accuracy, precision, true positive rate or recall, and false positive rate.