A sample classification algorithm based on inclusion degree

The purpose of this paper is to establish sample classification algorithms in consistent and inconsistent decision tables. First, according to the definition of inclusion degree and the idea of positive region in rough set, we give the definition of set value vector inclusion degree; then, according to the maximum inclusion degree principle, the sample classification algorithms are put forward with respect to consistent and inconsistent decision tables respectively; at last, the validity of the classification algorithms are accounted for through examples.