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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (5): 52-62.doi: 10.6040/j.issn.1671-9352.0.2023.398

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基于优化可辨识矩阵的多粒度粗糙集属性约简算法

宋苏洋1,叶军1,2*,曾广财1,孙清1   

  1. 1.南昌工程学院信息工程学院, 江西 南昌 330000;2.江西省水信息协同感知与智能处理重点实验室, 江西 南昌 330000
  • 发布日期:2024-05-09
  • 通讯作者: 叶军(1968— ),男,教授,硕士生导师,硕士,研究方向为粗糙集与粒计算理论等. E-mail:2003992646@nit.edu.cn
  • 基金资助:
    江西省教育厅科技资助项目(GJJ211920)

Multi-granularity rough set attribute reduction algorithm based on optimized discernibility matrix

SONG Suyang1, YE Jun1,2*, ZENG Guangcai1, SUN Qing1   

  1. 1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330000, Jiangxi, China;
    2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330000, Jiangxi, China
  • Published:2024-05-09

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摘要： 为了解决多粒度粗糙集中构造可辨识矩阵计算量过大等问题,提出了一种基于优化可辨识矩阵的改进的多粒度属性约简算法。使用属性重要度作为相似度构造不同粒度空间,输出各粒度空间的优化可辨识矩阵中的核属性,用于求解最终约简,对约简集进行反向冗余检测,避免存在冗余属性。结果表明:该算法能够有效降低时间复杂度,提升约简效率。实例和多个UCI数据集的实验结果验证了该算法的有效性。

关键词: 多粒度粗糙集, 粒度空间, 优化可辨识矩阵, 属性约简算法

Abstract: An improved multi-granularity attribute reduction algorithm based on optimized discernible matrix is proposed to solve the problem of excessive computation for constructing discernible matrix in multi-granularity rough sets. Attribute importance is used as similarity to construct different particle size spaces, and kernel attributes in the optimized discernibility matrix of each particle size space are output to solve the final reduction, and reverse redundancy detection is performed on the reduced set to avoid redundant attributes. The results show that this algorithm can effectively reduce the time complexity and improve the reduction efficiency. Examples and experimental results of several UCI data sets demonstrate the effectiveness of the proposed algorithm.

Key words: multi-granulation rough sets, granularity spaces, optimized discernibility matrix, attribute reduction algorithm

中图分类号: 

  • TP181
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