JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 52-62.doi: 10.6040/j.issn.1671-9352.0.2023.398

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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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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

CLC Number: 

  • TP181
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