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Variations of Neighbor Diversity for Fraudster Detection in Online Auction
Published: 03 November 2014 by MDPI in 1st International Electronic Conference on Entropy and Its Applications session Machine Learning and Systems Theory
Abstract: Inflated reputation fraud is a serious problem in online auction. Recently, the neighbor diversity based on Shannon entropy has been proposed as an effective feature to discern fraudsters from normal users. In the literature, there exist many different methods to quantify diversity. This raises the problem of finding the most suitable method to calculate neighbor diversity for fraudster detection. In this study, we collect four different methods of quantifying diversity, and apply them to calculate neighbor diversity. We then use these various neighbor diversities for fraudster detection. Our experimental results against a dataset collected from a real world auction website show that, although these diversities are calculated differently, their performances on fraudster detection are similar.
Keywords: Online auction; fraudster detection; neighbor diversity; entropy