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Proceedings Paper

Attribute reduction based on improved information entropy
Author(s): Baohua Liang; Fei Ruan II; Yun Liu III
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Paper Abstract

Traditional information entropy algorithm only considers the size of knowledge granularity, algebraic view only considers the impact of attributes on the determined subsets in the domain. In order to find an objective and comprehensive measure about the importance of attributes, first of all, starting from the algebraic view, we propose the definition of approximate boundary viscosity. Secondly, according to the definition of relative fuzzy entropy, the concept of relative information entropy is proposed, which can effectively measure the importance of attributes. In order to further enhance the importance of attributes, a concept of enhanced information entropy with significant amplification is proposed based on relative information entropy. Thirdly, two new attribute reduction methods are proposed by combining the approximate boundary precision with the entropy of relative information entropy and enhanced information entropy. Making full use of the results of U/B when seeking U / (B∪b) , greatly reducing the time overhead of the system. Finally, through experimental analysis and comparison, the feasibility and validity of the proposed algorithm in reducing quality and classification accuracy are verified.

Paper Details

Date Published: 26 July 2018
PDF: 9 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082817 (26 July 2018); doi: 10.1117/12.2501786
Show Author Affiliations
Baohua Liang, Chaohu College of Information Engineering (China)
Chaohu College Network Distributed Institute (China)
Fei Ruan II, Chaohu College of Information Engineering (China)
Yun Liu III, Chaohu College of Information Engineering (China)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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