Shill bidder detection for online auctions

Authors:
Tsuyoshi Yoshida;Hayato Ohwada
Affiliations:
Department of Industrial Administration, Faculty of Science and Technology, Research Institute for Science and Technology, Tokyo University of Science, Japan;Department of Industrial Administration, Faculty of Science and Technology, Research Institute for Science and Technology, Tokyo University of Science, Japan
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

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Abstract

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 oneclass 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.