A Detector and Evaluation Framework of Abnormal Bidding Behavior Based on Supplier Portrait

Xinqiang Ma, Xuewei Li, Baoquan Zhong, Yi Huang, Ye Gu, Maonian Wu, Yong Liu and Mingyi Zhang
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Xinqiang Ma: College of Computer Science and Technology, Guizhou University, China
Xuewei Li: State Grid Zhejiang Procurement Company, China
Baoquan Zhong: Institute of Cyber-Systems and Control, Zhejiang University, China
Yi Huang: College of Artificial Intelligence, Chongqing University of Arts and Sciences, China
Ye Gu: State Grid Zhejiang Procurement Company, China
Maonian Wu: School of Information Engineering, Huzhou University, China
Yong Liu: Institute of Cyber-Systems and Control, Zhejiang University, China
Mingyi Zhang: College of Computer Science and Technology, Guizhou University, China

International Journal of Information Technology and Web Engineering (IJITWE), 2021, vol. 16, issue 2, 58-74

Abstract: In a large number of bidding supplier groups, it is difficult to accurately find suppliers with unreasonable bidding behavior. In order to solve the problem of precise positioning of massive abnormal bidding behavior groups of diverse and widely distributed suppliers, the authors design a detector framework of abnormal bidding behavior based on supplier portrait. This paper mainly focuses on three abnormal bidding behaviors which harmful to the tenderers—“affiliated operation,” “subcontracting behavior,” and “colluding behavior.” Based on the bidding behavior records of suppliers, this paper establishes supplier portraits in four dimensions. In order to solve the problem that the detection algorithm under the unlabeled data is difficult to verify, this research establishes a new evaluation framework based on the bid base price formula and benefit map database of the supplier. The experiment verifies that the framework can effectively detect most suppliers with abnormal bidding behavior and can significantly change the benchmark price after eliminating abnormal suppliers.

Date: 2021
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