IDEAS home Printed from https://ideas.repec.org/a/igg/jitwe0/v16y2021i2p58-74.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xinqiang Ma & Xuewei Li & Baoquan Zhong & Yi Huang & Ye Gu & Maonian Wu & Yong Liu & Mingyi Zhang, 2021. "A Detector and Evaluation Framework of Abnormal Bidding Behavior Based on Supplier Portrait," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 16(2), pages 58-74, April.
  • Handle: RePEc:igg:jitwe0:v:16:y:2021:i:2:p:58-74
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITWE.2021040104
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jitwe0:v:16:y:2021:i:2:p:58-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.