A new approach for selecting attributes based on rough set theory

Decision trees are widely used in data mining and machine learning for classification. In the process of constructing a tree, the criteria of selecting partitional attributes will influence the classification accuracy of the tree. In this paper, we present a new concept, weighted mean roughness, which is based on rough set theory, for choosing attributes. The experimental result shows that compared with the entropy-based approach, our approach is a better way to select nodes for constructing decision trees.