  This paper present a strong data mining method based on rough set, which can
realize feature selection, classification and knowledge representation at the
same time. Rough set has good interpretability, and is a popular method for
feature selections. But low efficiency and low accuracy are its main drawbacks
that limits its application ability. In this paper,corresponding to the
accuracy, we first find the ineffectiveness of rough set because of
overfitting, especially in processing noise attribute, and propose a robust
measurement for an attribute, called relative importance.we proposed the
concept of "rough concept tree" for knowledge representation and
classification. Experimental results on public benchmark data sets show that
the proposed framework achieves higher accurcy than seven popular or the
state-of-the-art feature selection methods.
