IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i1d10.1007_s00180-024-01471-8.html
   My bibliography  Save this article

Imbalanced data sampling design based on grid boundary domain for big data

Author

Listed:
  • Hanji He

    (South China University of Technology)

  • Jianfeng He

    (South China University of Technology)

  • Liwei Zhang

    (Ping An Insurance Company of China)

Abstract

The data distribution is often associated with a priori-known probability, and the occurrence probability of interest events is small, so a large amount of imbalanced data appears in sociology, economics, engineering, and various other fields. The existing over- and under-sampling methods are widely used in imbalanced data classification problems, but over-sampling leads to overfitting, and under-sampling ignores the effective information. We propose a new sampling design algorithm called the neighbor grid of boundary mixed-sampling (NGBM), which focuses on the boundary information. This paper obtains the classification boundary information through grid boundary domain identification, thereby determining the importance of the samples. Based on this premise, the synthetic minority oversampling technique is applied to the boundary grid, and random under-sampling is applied to the other grids. With the help of this mixed sampling strategy, more important classification boundary information, especially for positive sample information identification is extracted. Numerical simulations and real data analysis are used to discuss the parameter-setting strategy of the NGBM and illustrate the advantages of the proposed NGBM in the imbalanced data, as well as practical applications.

Suggested Citation

  • Hanji He & Jianfeng He & Liwei Zhang, 2025. "Imbalanced data sampling design based on grid boundary domain for big data," Computational Statistics, Springer, vol. 40(1), pages 27-64, January.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01471-8
    DOI: 10.1007/s00180-024-01471-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01471-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-024-01471-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01471-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.