Performance Analysis of NB Tree, REP Tree and Random Tree Classifiers for Credit Card Fraud Data

Credit card fraud has been growing tremendously mainly in recent years. Due to this inconceivable occurrences, along with financial losses of banks, companies, NGOs and personal accounts, the reputation of the organizations is at stake. In this respect to detect fraud, accuracy has become very important to avoid harassing innocent customers. In this paper, we elicit three tree-based classifiers (NB Tree, REP Tree, and Random Tree) regarding the German credit card data set. We have combined this tree classifier with ensemble techniques to get better accuracy. Using the Resampling, Random Committee and Logit Boost, we were able to increase the performance of those classifiers.