In order to overcome the premature convergence and poor running efficiency of the attribute reduction algorithm based on particle swarm optimization, based on some special searching optimization advantages of the niche technology, a novel incomplete attribute reduction robust algorithm (named NCNPSO-IAR) of niche conic neighborhood particle swarm optimization was proposed. It could construct niche subvector of neighborhood radius by the layered conic space. The main advantages of the proposed algorithm involves to partition the adaptive niche radius by avoiding depending on the prior domain knowledge and to overcome the premature convergence.It could maintain the diversity of populations, and improve the running converge speed. Further, reduction set subvectors could share some cooperative social cognition in their conic subspaces, so as to get the optimization attribute reduction sets. Experimental results demonstrated that the proposed algorithm is efficient and robust, especially for the incomplete and noisy attribute reduction.
