计算机科学与探索Issue(1)：14-23,10.DOI:10.3778/j.issn.1673-9418.1407038
k-近邻模糊粗糙集的快速约简算法研究��
Fast Reduction Algorithm Research Based on k-Nearest Neighbor Fuzzy Rough Set
摘要
Abstract
Now a lot of generalized models of rough set are proposed by introducing some parameters to deal with the problems with noise. Traditional reduction algorithms are designed to find the minimum subset which keeps the information invariant. However, there is an obvious weakness that the algorithms have to be executed from the beginning on different parameters. This paper introduces the theoretical results of nested structure into the robust fuzzy rough set (i.e., k-nearest neighbor fuzzy rough sets), and then designs a fast reduction algorithm based on given reduction by using the nested structure. The main contribution of the proposed algorithm is that it can quickly find a reduction on different parameters when one reduction on certain parameter is already given. The numerical experiments verify that the executing time can be significantly saved through using fast reduction algorithm and demonstrate that the proposed algorithm is feasible and effective.关键词
k-近邻模糊粗糙集/属性约简/嵌套结构Key words
k-nearest neighbor fuzzy rough set/attribute reduction/nested structure分类
信息技术与安全科学引用本文复制引用
张照星,范星奇,赵素云,陈红,李翠平,孙辉..k-近邻模糊粗糙集的快速约简算法研究��[J].计算机科学与探索,2015,(1):14-23,10.基金项目
The National Natural Science Foundation of China under Grant Nos.61202114,61070056,61033010,60903089,60903088(国家自然科学基金) （国家自然科学基金）
the National Basic Research Program of China under Grant No.2012CB316205(国家重点基础研究发展计划(973计划)) （国家重点基础研究发展计划(973计划）
the Fundamental Research Funds for the Central Universities of China and the Research Funds of Renmin University of China under Grant No.10XNI018(中央高校基本科研业务费专项资金,中国人民大学研究基金) （中央高校基本科研业务费专项资金,中国人民大学研究基金）
