Rough Reduction in Fuzzy Information Systems
Guan Tao,Xue Liang,Feng Boqin
(School of Electronics and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,
China)
Abstract:Based on rough set theories and fuzzy equivalence relations,a knowledge
reduction method and importance degree of attributes under different granular partitions
of the object space in fuzzy information systems(FISs)were presented.Two parameters for
different level partitions (or similar degree between objects)¦Á,¦Âare used in these
reductions,in which the positive region formula of the decision level set are adopted for
relative reduction and importance degree of attributes, and the distributed reduction and
the assignment reduction are obtained by using rough membership functions of the
horizontal set.These reductions extend the attribute deduction methods in Pawlak
information systems(PISs)and provide new tools for knowledge discovery and feature
selection in FISs.Moreover, by using equivalence classes under different granules,the
discernment attribute matrix and discernment formula of the distributed reduction and the
assignment reduction were given, which overcome the inapplicability of classical methods
in FISs. Demonstration results show that the attribute subsets having maximum degree of
discernment and rule confidence with regard to all attributes can be produced by using
these methods in different granular spaces.
Keywords:fuzzy information system;relative reduction;distribution reduction;assignment
reduction