Research on Optimization Algorithm for Attribute Reduction of Decision System

Attribute reduction is one of the central issue in the theoretical research of rough set, aiming at the NP-hard problem of acquiring minimal reduction and all reduction, an optimization algorithm is proposed to construct reduction tree based on discernibility matrix for acquiring attribute reduction. The discernibility set is acquired by improving discernibility matrix first, then the core and reduction candidate information are obtained, finally the minimal reduction and all reduction are sought fast and effectively by considering attribute frequency as heuristic information, and time cost of the algorithm is analyzed. The time complexity of the algorithm is lower than other presented algorithms.