抄録
The classical rough set model has been generalized in many ways. The equivalence relation underlying the classical rough set model is generalized to a tolerance relation, a similarity relation, a dominance relation and to general relations. Moreover, the partition equivalent to the equivalence relation underlying the classical rough set model is generalized to a cover and families. Those relation-based generalization and family-based generalization depend on the interpretations of rough set models: classification and approximation. On the other hand, lower and upper approximations are generalized to positive and nonnegative regions by relaxing their conditions to be a member so as to accept some errors. The models generalized in this way are called variable-precision rough set models. In this paper, we combine the variable-precision type generalization with the relation-based and family-based generalizations. We investigate the variable-precision generalized rough set models from classification-oriented and approximation-oriented views. Several possible definitions are explored and their properties are shown. I
