Two novel feature selection methods based on decomposition and composition

Feature selection is a key issue in the research on rough set theory. However, when handling large-scale data, many current feature selection methods based on rough set theory are incapable. In this paper, two novel feature selection methods are put forward based on decomposition and composition principles. The idea of decomposition and composition is to break a complex table down into a master-table and several sub-tables that are simpler, more manageable and more solvable by using existing induction methods, then joining them together in order to solve the original table. Compared with some traditional methods, the efficiency of the proposed algorithms can be illustrated by experiments with standard datasets from UCI database.