Abstract
Abstract This paper proposes to design a majority vote ensemble classifier for accurate detection of credit card frauds. In this technique, the behaviour, operational and transactional features of users are combined into a single feature. The user behaviours over a banking website are collected and so that normal and abnormal behaviours of users are classified using Web Markov Skeleton Process (WMSP) model. The operational and transaction features of users are collected and classified using the Random Forest (RF) classifier and Support Vector Machine (SVM), respectively. Finally, the classification results of WMSP, RF and SVM are passed on to the majority voting based ensemble (MVE) classifier, which accurately predicts fraud users. By experimental results, it was shown that the MVE classifier achieve higher detection rate with good accuracy.
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