Risk prediction of Shill bidding fraud based on support vector machine

Shancheng Lin; Hongyu Lv; Xinyan Liu; Xiao Ruan; Ning Ding

doi:10.1117/12.2634840

4 May 2022 Risk prediction of Shill bidding fraud based on support vector machine

Shancheng Lin, Hongyu Lv, Xinyan Liu, Xiao Ruan, Ning Ding

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217211 (2022) https://doi.org/10.1117/12.2634840
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

In recent years, the auction industry has developed rapidly, and online auctions have become increasingly popular. However, the development of online auctions has also brought risks such as Shill bidding. This paper builds a Shill bidding prediction model based on support vector machine algorithm to solve the problem of difficulty in predicting Shill bidding behavior. Through the sorting and analysis of the characteristic data in the Shill bidding cases, ten indicators that are significantly related to the Shill bidding behavior have been obtained. In order to overcome the imbalance problem of the training set, a sampling balance mechanism is introduced to sample the data set. By comparing the calculation results of logistic regression and naïve Bayes algorithm, it is found that the support vector machine algorithm has the highest accuracy of Shill bidding risk prediction, reaching more than 99.2%. This study could not only improve the auction industry's ability to monitor, analyse, and judge the early warning, monitoring, analysis and judgment of bidding behavior. It could also guarantee the healthy and sound development of bidding work, and play a role in escorting social and economic development.

Citation Download Citation

Shancheng Lin, Hongyu Lv, Xinyan Liu, Xiao Ruan, and Ning Ding "Risk prediction of Shill bidding fraud based on support vector machine", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217211 (4 May 2022); https://doi.org/10.1117/12.2634840

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