Prof. Baohua Liang Profile

Prof. Baohua Liang

at Chaohu Univ

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Proceedings Article | 26 July 2018 Paper

Attribute reduction based on improved information entropy

Baohua Liang, Fei Ruan, Yun Liu

Proceedings Volume 10828, 1082817 (2018) https://doi.org/10.1117/12.2501786

KEYWORDS: Fuzzy logic, Information theory, Algorithms

Read 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.

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