Optimal Scale Combinations and Attribute Reduction for Consistent Generalized Multi-scale Ordered Fuzzy Decision Systems
Aiming at knowledge acquisition in generalized multi-scale ordered fuzzy decision systems,dominance relations in generalized multi-scale ordered fuzzy decision systems are firstly defined,information granules with different scale combinations in these systems are then constructed.Lower and upper approximations of sets with respect to dominance relations determined by an attribute set under different scale combinations are also defined.Five concepts of optimal scale combinations in consistent generalized multi-scale ordered fuzzy decision systems are defined.The numerical characteristics of these optimal scale combinations are described by belief and plausibility functions in the evidence theory.It is proved that belief optimal scale combinations are equivalent to lower approximate optimal scale combinations,and plausibility optimal scale combinations are equivalent to upper approximate optimal scale combinations.An attribute reduction approach based on a belief optimal scale combination is explored,and optimal scale combinations and attribute reduction search algorithms are formulated.Finally,experiments on UCI datasets verify the feasibility and validity of the proposed method and algorithms.
Evidence TheoryGranular ComputingMulti-scale Ordered Information SystemsOpti-mal Scale CombinationsRough Sets
NETL
NSTL
万方数据
