Hybrid attributes reduction based on neighborhood granulation and niche PSO algorithm

Hybrid decision systems include character attributes and numerical attributes. The lost of information when discretize the numerical attributes by Pawlak rough set is introduced. A reduction algorithm based on the neighborhood rough set model and the niche particle swarm optimization (PSO) algorithm is proposed. The affection of neighborhood operator to the reduction and classification is discussed also. Numerical attributes can be dealt directly by neighborhood relations. The PSO algorithm is a global optimization algorithm and can get all reductions. The use of the niche technology can avoid the premature convergence of the PSO. Experimental results demonstrate the validity and feasibility of the proposed algorithm, in application to four University of California at Irvine (UCI) machine learning databases.