Identification and prevention of shill bidding behavior based on XGBoost algorithm

Shancheng Lin; Hongyu Lv; Yadi Wang; Xuzhe Shang; Ning Ding

doi:10.1117/12.2656776

13 January 2023 Identification and prevention of shill bidding behavior based on XGBoost algorithm

Shancheng Lin, Hongyu Lv, Yadi Wang, Xuzhe Shang, Ning Ding

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101J (2023) https://doi.org/10.1117/12.2656776
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

It is difficult to track and identify shill bidding behavior brought by the development of online auctions. In order to solve this problem, this paper used the XGBoost algorithm to build a shill bidding pre-warning model and obtained the important characteristics of identifying this behavior. By comparing with other mainstream algorithms, it is found that the XGBoost algorithm has the highest accuracy in predicting the risk of shill bidding behavior, reaching 99.6%. Through the experiment and comparison of the number of features, three key indicators for identifying shill bidding behavior are found, which provides an accurate range for the attack and prevention. The research in this paper improves the identification ability of shill bidding behavior and reduces the scope of the identification characteristics of shill bidding behavior, which will effectively curb the continuous spread of shill bidding behavior.

Citation Download Citation

Shancheng Lin, Hongyu Lv, Yadi Wang, Xuzhe Shang, and Ning Ding "Identification and prevention of shill bidding behavior based on XGBoost algorithm", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101J (13 January 2023); https://doi.org/10.1117/12.2656776

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