基于粒计算与区分能力的属性约简算法  

An Attribute Reduction Algorithm Based on Granular Computing and Discernibility

作　　者：冀素琴[1] 石洪波[1] 吕亚丽[1] 

机构地区：[1]山西财经大学信息管理学院,太原030031

出　　处：《模式识别与人工智能》2015年第4期327-334,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60873100);山西省自然科学基金项目(No.2013011016-4;2014011022-2)资助

摘　　要：传统的属性约简方法将整个数据集一次性装入内存,很难适应大数据背景下的数据分析.为此文中提出基于粒计算与区分能力的属性约简算法.该算法运用统计学中的分层抽样技术,拆分原始大数据集为多个样本子集(粒),在每个粒上运用属性的区分能力进行属性约简,最后将各粒约简结果进行加权融合,得到原始大数据集的属性约简结果.实验表明该算法对海量数据集进行属性约简的可行性和高效性.In traditional attribute reduction algorithms, all the data are loaded into the main memory once, which is hard to adapt to the big data analyses. Aiming at this problem, an attribute reduction algorithm based on granular computing and discernibility is proposed. An original large-scale datset is divided into small granularities by applying stratified sampling in statistics, and then attributes are reduced on each small granularity based on discernibility of attribute. Finally, all the reductions on small granularities are fused by weighting. Experimental results show that the proposed algorithm is feasible and efficient for attribute reduction on massive datasets.

关 键 词：海量数据 粒计算 属性约简 分层抽样 区分能力 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]