Shill Bidder Detection for Online Auctions
Recently, the online auction has become a popular Internet service. Since the service has been expanded rapidly, security risks in the system remain. Fundamental measures are still required. This paper proposes a method for detecting shill bidders in online auctions. It first detects outliers with a one-class SVM. It then transforms the results into a decision tree using C4.5. The experiment results demonstrate that we can use the resulting rules to classify shill bidders.
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