基于标签树的粗糙集模型LTRS

Rough set model based on the labelled tree

为了刻画和处理半结构化数据的含糊、不确定性问题,针对这类半结构化数据模型中所蕴含的组成结构和内容信息,扩展了传统的粗糙集模型,提出了一种基于标签树的粗糙集模型LTRS(labelled tree rough set model)。利用标签树的结构和内容,重新定义了等价关系、不可区分关系、上、下近似集合等粗糙集基本概念。进一步描述了区分矩阵和决策规则,并且以某地区的流行性乙型脑炎个案XML调查表组成的标签树信息系统为例,依据定义给出了决策规则的抽取,所产生的规则可用于指导乙型脑炎的临床分型。

In order to characterize and deal with the vagueness and uncertainty of structured data as well as the compositions and contents implied within semi-structured data models,a labelled tree rough set model（LTRS） was presented by extending the traditional rough set model.Making use of the structure and content of the labelled tree,the basic concepts of rough set were redefined,such as equivalence relation,indiscernibility relation,upper approximation and lower approximation,etc.Furthermore,the discernibility matrix and decision rules were described.Using the labeled tree constructed by XML case questionary of epidemic encephalitis B from some area as an example,the extraction method of decision rules was presented based on the definitions given above.The decision rules produced by LTRS can be used to guide the clinic classification in the case of epidemic encephalitis B.