[1]陈 妍,宋晶晶,杨习贝.约简加速求解的属性簇方法[J].南京理工大学学报(自然科学版),2020,44(02):216-223.[doi:10.14177/j.cnki.32-1397n.2020.44.02.013]
 Chen Yan,Song Jingjing,Yang Xibei.Accelerator for finding reduct based on attribute group[J].Journal of Nanjing University of Science and Technology,2020,44(02):216-223.[doi:10.14177/j.cnki.32-1397n.2020.44.02.013]
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约简加速求解的属性簇方法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年02期
页码:
216-223
栏目:
出版日期:
2020-04-30

文章信息/Info

Title:
Accelerator for finding reduct based on attribute group
文章编号:
1005-9830(2020)02-0216-08
作者:
陈 妍1宋晶晶12杨习贝1
1.江苏科技大学 计算机学院,江苏 镇江 212003; 2.数据科学与智能应用福建省高校重点实验室,福建 漳州 363000
Author(s):
Chen Yan1Song Jingjing12Yang Xibei1
1.School of Computer,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.Key Laboratory of Data Science and Intelligent Application of Fujian Province University, Zhangzhou 363000,China
关键词:
属性簇 属性约简 桶模型 邻域粗糙集
Keywords:
attribute groups attribute reduction bucket model neighborhood rough set
分类号:
TP181
DOI:
10.14177/j.cnki.32-1397n.2020.44.02.013
摘要:
为了进一步提高约简求解的效率,该文在桶模型的基础上,从数据中属性间的相似性程度出发,将属性划分为不同的簇,使得在约简的搜索进程中,只需以属性簇为基准进行候选属性的筛选即可达到压缩属性搜索空间的目的。实验结果表明,无论是采用传统的邻域计算或是基于桶模型的邻域计算,在不降低分类性能的前提下,基于属性簇的搜索策略都能显著降低求解约简的时间消耗。该文研究可从样本和属性两方面为约简求解加速提供参考。
Abstract:
To improve the time efficiency of obtaining the reducts,based on the mechanism of bucket model,attributes are divided into different groups by considering the similarity between attributes. It follows that in the searching process of deriving reducts,attributes out of those groups containing at least one attribute in potential reducts should be evaluated,which can effectively reduce the searching space. Compared with the forward greedy searching approach,the experimental results show that the proposed strategy can significantly reduce the time consumption of obtaining reducts and the classification performance of reducts derived by using the strategy of attribute group is not decreased. This study provides a useful idea for accelerating the process of finding reducts.

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相似文献/References:

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 ZhouXianzhong HuangBing.Rough Set-based Attribute Reduction under Incomplete Information Systems[J].Journal of Nanjing University of Science and Technology,2003,(02):630.

备注/Memo

备注/Memo:
收稿日期:2019-10-28 修回日期:2020-02-03
基金项目:国家自然科学基金(61906078; 61572242); 数据科学与智能应用福建省高校重点实验室开放课题(D1901)
作者简介:陈妍(1996-),女,硕士生,主要研究方向:粗糙集与粒计算,E-mail:yanchenedu@163.com; 通讯作者:宋晶晶(1990-),女,博士,讲师,主要研究方向:粒计算、粗糙集与机器学习,E-mail:songjingjing108@163.com。
引文格式:陈妍,宋晶晶,杨习贝. 约简加速求解的属性簇方法[J]. 南京理工大学学报,2020,44(2):216-223.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-04-20