关键词: 邻域粗糙模型，决策依赖度，固有维数估算，极大似然估计法，粒子群优化算法，粒子群快速约简算法

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