近似边界精度信息熵的属性约简

Attribute reduction based on information entropy of approximation boundary accuracy

针对信息观只考虑知识粒度的大小,不能客观、全面度量属性重要性的不足,首先从代数观出发,提出近似边界精度的定义;其次,根据相对模糊熵的定义,提出相对信息熵及增强信息熵概念,与相对模糊熵相比具有明显的放大作用;再次,将近似边界精度融合相对信息熵和增强信息熵,提出两种新的属性约简方法,在求U/(B∪b)时充分利用U/B的结果,可大大减少系统的时间开销;最后,通过实验分析和比较,本文算法在约简质量、分类精度上的可行性和有效性得到了验证.

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 U 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.