A performance analysis of different ensemble and hybrid techniques in credit card fraudulent transactions

Recently, the use of payment mediums like credit cards have increased and thus the number of fraudulent transactions as well. In this situations where anomaly detection is very important, an imbalanced dataset can lead to a drastic fall in the performance of the classification algorithms. If the majority instances outnumber the number of samples in minority class it is known as class imbalance. The ensemble techniques with and without sampling have been executed and it is observed that with the traditional ensemble techniques to detect credit card frauds, the performance of some of the algorithms is considerably low as compared to others. Thus, with the aim to improve the performances of these algorithms, a hybrid approach has been proposed, where two of the worst-performing ensemble techniques have been taken based on their F1-Score. This model takes the output of these two ensemble techniques and predicts the final output based on a logical connector. It is observed that in some of the cases, the improvement in the F1-Score is as high as 15.36%, whereas in some other cases the improvements are very minute.