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 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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Yoshida, T., Ohwada, H. (2010). Shill Bidder Detection for Online Auctions. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_33
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DOI: https://doi.org/10.1007/978-3-642-15246-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15245-0
Online ISBN: 978-3-642-15246-7
eBook Packages: Computer ScienceComputer Science (R0)