基于限制非对称相似关系的粗糙集模型

基于不可分辨关系的粗糙集理论不适用于含未知值的不完备信息系统.需要将经典的粗糙集理论不可分辨关系加以扩充才能处理不完备信息系统.目前已经提出了基于容差关系、量化容差关系、限制容差关系、非对称相似关系等的扩充粗糙集理论.但是,这些理论还存在一些局限性.文章提出了一种新的基于限制非对称相似关系的粗糙集扩充模型.理论分析和实验证明,与其它模型相比,可以从基于限制非对称相似关系模型的近似集中获取更多的信息.

Rough Set Model Based on Limited Non-symmetric Similarity Relation

The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analyzing incomplete information systems where some values of attributes are unknown. In order to process incomplete information systems, the classical rough set theory needs to be extended to some unequal relation. Several extension models had been researched such as tolerance relation, non-symmetric similarity relation, valued tolerance relation and limited tolerance relation. However, these extensions have their own limitation. In this paper, a new extension of rough set which is based on a limited non-symmetric similarity relation was developed. A theory analysis and the computational experiments show that for the limited non-symmetric similarity relation approach it is possible to obtain more information approximations compared with the other models.