Attribute reduction based on the self-information entropy of intuitionistic fuzzy decision systems
In the context of intuitionistic fuzzy sets,the reduction based on lower approximation only considers the lower approximation and ignores the upper approximation,resulting in certain information loss.To address this issue,three entro-py measures based on upper and lower approximations of intuitionistic fuzzy sets are presented and applied to the reduction of intuitionistic fuzzy decision information systems.Three uncertainty measures are defined on the intuitionistic fuzzy deci-sion information system to describe intuitionistic fuzzy relationships,namely the average decision index,the average safe decision index,and the average risk decision index.Based on these measures,conditional information entropy,conditional rough entropy,and self-information entropy are subsequently introduced.Based on the self-information entropy,a definition of attribute reduction and an attribute reduction algorithm are proposed.Experiments on multiple datasets show that the pro-posed attribute reduction algorithm exhibits superior and robust reduction results compared to other algorithms.
attribute reductionintuitionistic fuzzy decision information systemconditional information entropyconditional rough entropyself-information entropy
万方数据
维普
