基于相似关系的局部粗糙集模型  

Local rough set model based on similarity relation

作　　者：张杰 张燕兰 ZHANG Jie;ZHANG Yan-lan(School of Computer Science,Key Laboratory of Data Science and Intelligence Application,Fujian Province University,Fujian Province Key Laboratory of Granular Computing and Its Application,Minnan Normal University,Zhangzhou 363000,Fujian,China)

机构地区：[1]闽南师范大学计算机学院,数据科学与智能应用福建省高等学校重点实验室,福建省粒计算及其应用重点实验室,福建漳州363000

出　　处：《山东大学学报（理学版）》2021年第3期77-82,共6页Journal of Shandong University(Natural Science)

基　　金：福建省高校杰出青年科研人才培养计划资助项目。

摘　　要：粗糙集理论是一种有监督学习模型,一般需要适量有标记的数据来训练分类器,但现实中的一些问题往往存在大量无标记的数据,若标记数据则代价过大。概念近似是粗糙集理论的一个关键所在,基于相似关系粗糙集的提出,扩大了粗糙集理论的应用范围。为了应对大数据标记特性有限和计算效率低的问题,本文介绍了一个相似关系下的局部粗糙集理论模型,提出了具有线性时间复杂度的概念近似模型,理论证明和实例分析验证了基于相似关系的局部粗糙集中概念近似模型的优越性。Rough set theory is a supervised learning model, which generally requires a certain amount of labeled data to train classifiers. However, there are many unlabeled data in some practical problems, and the cost of labeling data is too large. The concept approximation is a key problem in rough set theory. The rough set based on similarity relation expands the application of rough set theory. In order to deal with the problem of limited label characteristics and low calculation efficiency, a theoretical model of local rough set under similarity relationship is introduced and a concept approximation model with linear time complexity is proposed. Theoretical proof and case analysis verify the superiority of local rough set based on similarity relation.

关 键 词：局部粗糙集 相似关系 概念近似 有限标记数据 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]