中文摘要：

阐述了粗糙集理论、遗传约简算法和粒子群约简算法。属性约简是知识发现的关键问题之一。传统的属性约简算法都是串行搜索的,算法效率低且收敛速度慢。将计算智能和粗糙集相结合,提出了一种基于遗传粒子群和粗糙集的最小属性约简算法。该算法利用属性依赖度计算属性核,并在种群初始化时引入属性核作为限制条件,动态调整适应度函数,以达到求得最小属性约简的目的。实验表明,对于数据量大、属性维度高的属性约简问题,该算法具有高效的处理能力。

英文摘要：

We exploit the basic concepts of the rough sets theory, genetic algorithm and particle swarm optimization algorithm. Attributes reduction is one of the key issues in knowledge discovery. Traditional reduction algorithms feature serial search, low efficiency and slow convergence speed. By combining computational intelligence and rough sets, we propose a minimum attributes reduction algo- rithm which bases on the rough sets, genetic algorithm and particle swarm optimization algorithm. In order to solve the minimum attribute reduction, this algorithm regulates the function parameters dynam- ically and calculates attribute core using attribute dependability, thus restricting the initialized popula- tion. Experimental results prove the efficiency of the proposed algorithm in attribute reduction for high dimensionality and big data.