Abstract：To get the attribute reduction in variable precision rough set model, an upper and lower approximation binary relation is defined on object sets. By applying the binary relation, the equivalence relation is constructed on attribute sets and thus a dependence space is produced. Then, theorems for judging upper and lower approximation consistent sets are obtained. Meanwhile, a new attribute reduction method is proposed to preserve some invariant characters of upper and lower approximation in each decision class. Finally, a practical example illustrates the validity of the proposed method.
余承依，李进金. 基于依赖空间的变精度粗糙集属性约简*[J]. 模式识别与人工智能, 2014, 27(12): 1065-1070.
YU Cheng-Yi, LI Jin-Jin. Attribute Reduction in Variable Precision Rough Set Based on Dependence Space. , 2014, 27(12): 1065-1070.
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2014, Vol. 27 
