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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.

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