Granular Computing

, Volume 4, Issue 3, pp 313–322 | Cite as

Attribute reduction based on the Boolean matrix

  • Yunpeng Shi
  • Yang Huang
  • Changzhong WangEmail author
  • Qiang He
Original Paper


Attribute reduction is an important preprocessing step in machine learning and pattern recognition. This paper introduces condition-attribute Boolean matrix and decision-attribute Boolean matrix and proposes a new attribute reduction model based on Boolean operations according to the concept of neighborhood rough set. Some operation rules of Boolean vectors are defined and an uncertainty measure, named attribute support function, is proposed to evaluate the importance degree of candidate attributes with respect to decision attributes. An attribute reduction algorithm based on the proposed measure is designed. Ten data sets selected from public data sources are used to compare the proposed algorithm with some existing algorithms. The experimental results show that the proposed reduction algorithm is feasible and effective.


Rough set Attribute reduction Boolean vector Boolean matrix 



This work was supported by the National Natural Science Foundation of China under Grants 61572082, 61673396, and 61473111, the Foundation of Educational Committee of Liaoning Province (LZ2016003), and the Natural Science Foundation of Liaoning Province (20170540012, 20170540004).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MathematicsBohai UniversityJinzhouPeople’s Republic of China
  2. 2.College of ScienceBeijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China

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