Journal of East China Normal University(Natural Science) >

2018 , Vol. 2018 >Issue 3: 97 - 108,156

DOI: https://doi.org/10.3969/j.issn.1000-5641.2018.03.011

Attribute reduction based on information entropy of approximation boundary accuracy

  • LIANG Bao-hua ,
  • WU Qi-lin
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  • 1. College of Information Engineering, Chaohu University, Hefei 238000, China;
    2. Institute of Networks and Distributed System, Chaohu University, Hefei 238000, China

Received date: 2017-04-21

  Online published: 2018-05-29

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Abstract

From an information point of view, only the size of knowledge granularity is taken into account, while the importance of attributes cannot be objectively and comprehensively measured. First, starting from the perspective of algebra, the concept of approximate boundary accuracy is proposed. Afterwards, according to the definition of relative fuzzy entropy, this paper proposes two new concepts for relative information entropy and enhanced information entropy. Compared with relative fuzzy entropy, the proposed information entropy has an obvious magnification effect. Two new methods of attribute reduction are subsequently proposed by incorporating approximate boundary accuracy into relative information entropy and enhanced information entropy. Computing U/(B∪b) while making full use of U/B can greatly reduce the computational overhead on time. Finally, through the experimental analysis and comparison, it is validated that the proposed algorithm has feasibility and effectiveness in both reduction quality and classification accuracy.

Key words： rough set; attribute reduction; approximation boundary accuracy; relative information entropy; enhanced information entropy

Cite this article

LIANG Bao-hua , WU Qi-lin . Attribute reduction based on information entropy of approximation boundary accuracy[J]. Journal of East China Normal University(Natural Science), 2018 , 2018(3) : 97 -108,156 . DOI: 10.3969/j.issn.1000-5641.2018.03.011

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