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Attribute reduction based on improved information entropy

机译：基于改进信息熵的属性约简

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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.
机译：传统的信息熵算法仅考虑知识粒度的大小，代数视图仅考虑属性对域中确定子集的影响。为了找到关于属性重要性的客观和全面的度量，首先，从代数观点出发，我们提出近似边界粘度的定义。其次，根据相对模糊熵的定义，提出了相对信息熵的概念，可以有效地度量属性的重要性。为了进一步增强属性的重要性，提出了一种基于相对信息熵的，具有显着放大作用的增强信息熵的概念。第三，提出了两种新的属性约简方法，将近似边界精度与相对信息熵和增强信息熵相结合。求U /（B∪b）时充分利用U / B的结果，大大减少了系统的时间开销。最后，通过实验分析和比较，验证了该算法在降低质量和分类精度上的可行性和有效性。

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