A Novel Classification Approach for C2C E-Commerce Fraud Detection

Abstract

Fraud in consumer-to-consumer (C2C) e-commerce is becoming more and more serious. The purpose of this study is to develop an effective fraud detection model to assist customers in identifying potential fraud transactions. We 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. The experimental results demonstrate that the model based on NB and AdaBoost&C4.5 can significantly increase the ability to locate potential fraud transactions in C2C e-commerce.