This paper introduces a new way for selecting attributes,called Sets Classify Roughness,which is based on Rough sets theory and the characteristic of the decision tree and can elevate the level of leaf node to a degree when the total number does not change.

The algorithm takes a novel measure-attribute classification rough degree as the heuristic of choosing attribute at a tree node, which more synthetically measures contribution of an attribute for classification than other measures in Rough set and is simpler in calculation than information gain an.

For the problem that the measures for measuring attribute classification ability in Rough set can only reflect the size of the object set discriminated by attributes but the synthetic contribution ability of attributes for classification,a new synthetic measures —attribute classification rough degree(ACRD) is proposed for measuring attribute classification contribution ability in Rough set.

This article expounds the basic notion of rough sets theory,and uses the principle of partial knowledge dependence of this theory.

The concepts and characterization of fuzzy and rough sets are presented in this paper.

A variant of TAN using rough sets theory is presented,and their tree classifier structures, which can be thought of as a selective restricted trees Bayesian classifier, are compared.

The Research of Classification Based on Rough Sets and Naive Bayes;

The Research on the Classification Model Based on Rough Set and Entropy;

On the basis of the combination of the theory of the rough set with that of the lattice machine and the mechanisms of both the rough set and the lattice machine, data reduction was carried out.