IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2628-d873125.html
   My bibliography  Save this article

Granular Elastic Network Regression with Stochastic Gradient Descent

Author

Listed:
  • Linjie He

    (College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China)

  • Yumin Chen

    (College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China)

  • Caiming Zhong

    (College of Science and Technology, Ningbo University, Ningbo 315211, China)

  • Keshou Wu

    (College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China)

Abstract

Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine. To address the problem that the traditional linear regression model is difficult to handle uncertain data, we propose a granule-based elastic network regression model. First we construct granules and granular vectors by granulation methods. Then, we define multiple granular operation rules so that the model can effectively handle uncertain data. Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. After that, we conduct the derivative of the granular loss function and design the granular elastic network gradient descent optimization algorithm. Finally, we performed experiments on the UCI datasets to verify the validity of the granular elasticity network. We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model.

Suggested Citation

  • Linjie He & Yumin Chen & Caiming Zhong & Keshou Wu, 2022. "Granular Elastic Network Regression with Stochastic Gradient Descent," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2628-:d:873125
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2628/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/15/2628/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    2. Xiyang Yang & Shiqing Zhang & Xinjun Zhang & Fusheng Yu, 2022. "Polynomial Fuzzy Information Granule-Based Time Series Prediction," Mathematics, MDPI, vol. 10(23), pages 1-21, November.

    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:15:p:2628-:d:873125. 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.