A Novel Classification Approach for C2C E-Commerce Fraud Detection
Fraud in Consumer-To-Consumer (C2C) e-commerce is becoming more and more serious. This paper is to develop an effective fraud detection model to assist customers in identifying potential fraud transactions. The authors use Naive Bayes (NB), decision tree C4.5 and AdaBoost to construct the model for classifying imbalance transaction data, and majority voting is used to combine the model. Several experiments are conducted on Taobao data set to verify the classification performance of the proposed model using four popular performance metrics.