A New Method of Data Mining Based on Rough Sets and Discrete Particle Swarm Optimization

摘要

A new rough set (RS) knowledge acquisition based on discrete particle swarm optimization(DPSO-RS) are proposed to solve feature selection strategy, rough set is lack of the ability of anti-jamming, which is used the information entropy is considered as a suitable function in discrete particle swarm algorithm and the attribute dependent degree of variable precision rough set is optimized, and make the classification rules more reliable in the case of noisy data. The study of knowledge acquisition method based on DPSO-RS algorithm which is applied into the grate-kiln system in order to acquire knowledge. Experimentation is carried out, using mass data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.