基于邻域粗糙集的连续值分布式数据属性约简

为了去除系统中的冗余属性,保持系统的分类能力,研究了连续值分布式数据的属性约简.给出了连续值分布式决策信息系统中邻域粗糙集的定义,讨论了分布式连续值决策信息系统中正域计算的可分解性.以保持分布式决策信息系统的正域不变为前提,探讨了分布式决策信息系统中属性的可约性,提出了分布式连续值决策信息系统的属性约简算法.为了验证该算法的有效性,在7份数据集上进行了3组实验.实验使用提出的算法对分布式数据进行属性约简,进而采用加权集成的方式进行分类测试.实验结果表明,该算法能够有效去除连续值分布式数据中的冗余属性,使得约简后的连续值分布式数据的集成分类能力与约简前相差不大.甚至更高.

Neighborhood rough set based attribute reduction for continuous-valued distributed data

In order to reduce the redundant attributes in a system, and keep the classification ability of the system unchanged, the attribute reduction of continuous-valued distributed data is studied in this paper. Firstly, the definition of neighborhood rough set in distributed continuous-valued decision information system is given, and it is proved that the positive region of distributed continuous-valued decision information system is additive. Then, the reducibility of attributes in distributed decision information system is discussed based on the precondition that the positive region of the system remains unchanged, and the attribute reduction algorithm of distributed continuous-valued decision information system is proposed. Finally, three groups of experiment are conducted on seven sets of data to prove the effectiveness of the algorithm proposed in this paper. In the experiments, a distributed continuous-valued distributed data is reduced by the algorithm proposed in this paper, and then the weighted integration method is used to test the classification ability of the reduced data. The experimental results show that the algorithm can effectively remove the redundant attributes of continuous valued distributed data and keep the classification ability of reduced data the same as the data before reduction, or even better.