IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i5p829-d764763.html
   My bibliography  Save this article

Cost-Sensitive Broad Learning System for Imbalanced Classification and Its Medical Application

Author

Listed:
  • Liang Yao

    (Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Pak Kin Wong

    (Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China)

  • Baoliang Zhao

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Ziwen Wang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Long Lei

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Xiaozheng Wang

    (Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China)

  • Ying Hu

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Pazhou Lab, Guangzhou 510320, China)

Abstract

As an effective and efficient discriminative learning method, the broad learning system (BLS) has received increasing attention due to its outstanding performance without large computational resources. The standard BLS is derived under the minimum mean square error (MMSE) criterion, while MMSE is with poor performance when dealing with imbalanced data. However, imbalanced data are widely encountered in real-world applications. To address this issue, a novel cost-sensitive BLS algorithm (CS-BLS) is proposed. In the CS-BLS, many variations can be adopted, and CS-BLS with weighted cross-entropy is analyzed in this paper. Weighted penalty factors are used in CS-BLS to constrain the contribution of each sample in different classes. The samples in minor classes are allocated higher weights to increase their contributions. Four different weight calculation methods are adopted to the CS-BLS, and thus, four CS-BLS methods are proposed: Log-CS-BLS, Lin-CS-BLS, Sqr-CS-BLS, and EN-CS-BLS. Experiments based on artificially imbalanced datasets of MNIST and small NORB are firstly conducted and compared with the standard BLS. The results show that the proposed CS-BLS methods have better generalization and robustness than the standard BLS. Then, experiments on a real ultrasound breast image dataset are conducted, and the results demonstrate that the proposed CS-BLS methods are effective in actual medical diagnosis.

Suggested Citation

  • Liang Yao & Pak Kin Wong & Baoliang Zhao & Ziwen Wang & Long Lei & Xiaozheng Wang & Ying Hu, 2022. "Cost-Sensitive Broad Learning System for Imbalanced Classification and Its Medical Application," Mathematics, MDPI, vol. 10(5), pages 1-19, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:829-:d:764763
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/5/829/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/5/829/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bolin Liao & Zhendai Huang & Xinwei Cao & Jianfeng Li, 2022. "Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach," Mathematics, MDPI, vol. 10(13), pages 1-14, June.

    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:gam:jmathe:v:10:y:2022:i:5:p:829-:d:764763. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.