多粒度决策形式背景的属性约简

Attribute Reduction in Multi-granularity Formal Decision Contexts

针对现有的决策形式背景属性约简方法不能处理多粒度数据的问题,文中提出3种多粒度决策形式背景的属性约简方法,目的是通过删除每个协调粒度层下相同类别的类属性块,实现信息系统的属性约简.首先从信息粒的角度出发,在多粒度决策形式背景中引入协调粒度层的信息熵及条件信息熵,利用它们进一步度量属性重要度.然后,在多粒度决策形式背景中基于平均条件信息熵、最粗协调决策形式背景条件信息熵及最细协调决策形式背景条件信息熵,提出协调粒度约简方法、最粗协调粒度约简方法、最细协调粒度约简方法及其实现算法.最后,通过实验验证文中提出的3种属性约简方法的有效性,对比这3种方法得到的属性约简集,发现协调粒度约简方法的约束条件较严,相比之下,最粗协调粒度约简方法和最细协调粒度约简方法约束条件相对宽松.

The existing attribute reduction methods for formal decision contexts cannot deal with multi-granularity data.Therefore,three attribute reduction methods are put forward in multi-granularity formal decision contexts to realize attribute reduction of an information system by removing the class-attribute blocks from the same category under each consistent granularity layer.Firstly,from the perspective of information granules,information entropy and conditional information entropy of the consistent granularity layer are introduced in the multi-granularity formal decision contexts to further measure the significance of attributes.Secondly,based on the average conditional information entropy and conditional information entropy in the coarsest and finest consistent formal decision contexts,the consistent granularity attribute reduction method and the coarsest and finest consistent granularity attribute reduction methods are proposed in multi-granularity formal decision contexts,and their corresponding implementation algorithms are developed.Finally,the experimental results show that the proposed attribute reduction methods are effective.In addition,it is concluded that the constraint of the consistent granularity attribute reduction method is too strict.Instead,the constraints of the coarsest and finest consistent granularity attribute reduction methods are relatively weaker.