Abstract: According to the particle swarm optimization algorithm's idea,a new algorithm(SPRA) to get a optimal attribute reduction on the high-dimensional neighborhood decision table was proposed. Through the use of intrinsic dimension analysis method, taking the intrinsic dimensionality estimated as the SPRA algorithm' s initialization parameter,a quick reduction algorithm(QSPRA) was proposed to deal with the high-dimensional data sets. hhe algorithm's validity was verified by five high-dimensional data sets from UCI. In the experimental analysis section, the population size and the number of iteration to the influence of the reduction result were also discussed. Moreover, the experiments also show that it is impossible to solve high-dimensional data sets based on kernel-based heuristic algorithm ideas.

Key words: Neighborhood rough set model, Decision-making dependency, Intrinsic dimension estimation, MLE, Particle swarm optimization algorithm, Quick particle swarm reduction algorithm