JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (1): 62-68.doi: 10.6040/j.issn.1671-9352.1.2019.006

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An incremental attribute reduction approach when attribute values and attributes of the decision system change dynamically

JING Yun-ge1*, JING Luo-xi2, WANG Bao-li1, CHENG Ni1   

  1. 1. Maths &
    Information Technology School, Yuncheng University, Yuncheng 044000, Shanxi, China;
    2. School of Software, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Published:2020-01-10

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Abstract: The incremental mechanisms to calculate relative knowledge granularity based on matrices are introduced when multiple attributes are added into decision system and attribute values refining. Then, the corresponding incremental attribute reduction method based on matrix is developed. Compared with the non-incremental attribute reduction algorithm and other incremental algorithms, the proposed incremental attribute reduction algorithm based on matrix can obtain a new reduct in a much shorter time. Finally, experiments on some data sets downloaded from UCI show that the proposed incremental attribute reduction method based on matrix is effective and efficient.

Key words: rough set, incremental learning, attribute reduction, knowledge granularity

CLC Number: 

  • TP18
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