JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (7): 104-115.doi: 10.6040/j.issn.1671-9352.8.2024.024

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Multi-granularity support vector regression algorithm based on granular ball computing

HUA Youlin1, SHAO Yabin1,2*, ZHU Xueqin1   

  1. 1. School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2. Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing 400065, China
  • Published:2025-07-01

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Abstract: To achieve both efficiency and robustness in the support vector regression algorithm, multi-granularity granular ball computing is integrated into the support vector regression algorithm. A multi-granularity granular ball support vector regression algorithm is proposed based on granular ball computing. The radius information from granular balls is incorporated into the models constraint conditions, replacing the traditional sample point based support vector regression algorithm with a granular ball based support vector regression algorithm. Additionally, the dual model of the multi-granularity granular ball support vector regression is investigated, and a particle swarm optimization algorithm is utilized to solve it. Experimental results show that on both artificial datasets and University of California-Irvine(UCI)publicly available datasets, computational efficiency and robustness are improved by the multi-granularity granular ball support vector regression.

Key words: data mining, granular computing, multi-granularity representation, multi-granularity granular ball computing, support vector regression

CLC Number: 

  • TP391
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