employs the neighborhood rough set reduction model which can process the numerical features directly without discretization. Then the particle fitness function in particle swarm optimization (PSO) algorithm is built based on that model. Finally, a novel feature selection algorithm based on particle swarm optimization and neighborhood rough set reduction model is proposed. Experimental results prove that the new algorithm improves classification ability with fewer features selected.

test\nrs.h
....\Pso.h
....\test.cpp
....\test.dsp
....\test.dsw
....\test.ncb
....\test.opt
....\test.plg
....\Utility.h
test

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