Rough set model based on the labelled tree - LI Xiong-fei; SUN Tao; GUO Jian-fang (Tongxin xuebao)
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.