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《山东大学学报(理学版)》 ›› 2025, Vol. 60 ›› Issue (7): 104-115.doi: 10.6040/j.issn.1671-9352.8.2024.024

• • 上一篇    

基于粒球计算的多粒度支持向量回归算法

华有霖1,邵亚斌1,2*,朱学勤1   

  1. 1.重庆邮电大学理学院, 重庆 400065;2.网络空间大数据智能安全教育部重点实验室, 重庆 400065
  • 发布日期:2025-07-01
  • 通讯作者: 邵亚斌(1974— ),男,教授,博士,研究方向为模糊分析学、粗糙集与粒计算、数据分析的不确定方法等. E-mail:shaoyb@cqupt.edu.cn
  • 作者简介:华有霖(2000— ),男,硕士研究生,研究方向为机器学习、数据分析的不确定方法等. E-mail:s220603003@stu.cqupt.edu.cn*通信作者:邵亚斌(1974— ),男,教授,博士,研究方向为模糊分析学、粗糙集与粒计算、数据分析的不确定方法等. E-mail:shaoyb@cqupt.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(12061067,62176033);重庆市自然科学基金面上资助项目(CSTB2023NSCQ-MSX0707)

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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摘要： 为了实现支持向量回归算法的高效性和鲁棒性,本文将多粒度粒球计算融合到支持向量回归算法中,提出了一种基于粒球计算的多粒度粒球支持向量回归算法。该算法将粒球中的半径信息加入到约束条件中,将原本基于样本点的支持向量算法替换为基于粒球的支持向量回归算法。同时,本文研究了多粒度粒球支持向量回归机的对偶模型。实验结果表明,采用人工数据集和加州大学欧文分校(University of California-Irvine, UCI)公开数据集时,多粒度粒球支持向量回归机的计算效率和鲁棒性均得到提升。

关键词: 数据挖掘, 粒计算, 多粒度表示, 多粒度粒球计算, 支持向量回归机

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

中图分类号: 

  • TP391
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