[1]罗来鹏,范自柱.粗糙集最优近似的动态更新方法[J].深圳大学学报理工版,2021,38(3):324-330.[doi:10.3724/SP.J.1249.2021.03324]
 LUO Laipeng and FAN Zizhu.A dynamic approach for updating the optimal approximation of rough set[J].Journal of Shenzhen University Science and Engineering,2021,38(3):324-330.[doi:10.3724/SP.J.1249.2021.03324]
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粗糙集最优近似的动态更新方法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第38卷
期数:
2021年第3期
页码:
324-330
栏目:
电子与信息科学
出版日期:
2021-05-14

文章信息/Info

Title:
A dynamic approach for updating the optimal approximation of rough set
文章编号:
202103016
作者:
罗来鹏范自柱
华东交通大学理学院,江西南昌 330013
Author(s):
LUO Laipeng and FAN Zizhu
School of Sciences, East China Jiaotong University, Nanchang 330013, Jiangxi Province, P.R.China
关键词:
人工智能粗糙集粒计算属性约简最优近似集增量更新相似度
Keywords:
artificial intelligence rough set granular computing attribute reduction optimal approximation set incremental updating similarity degree
分类号:
TP391
DOI:
10.3724/SP.J.1249.2021.03324
文献标志码:
A
摘要:
目标概念在近似空间上的近似表示与计算是粗糙集理论模型研究的基础.对于动态属性约简及其相关问题往往需要更新粗糙集理论中目标概念的近似性,现有的更新方法都基于目标概念的上、下近似.提出一种基于最优近似的动态更新方法,针对新增单个对象引起决策系统中条件类与决策类的动态变化,讨论4种不同情形下决策类的最优近似动态更新机制,由此得到一种动态的最优近似集更新算法.在 UCI 数据集上的测试结果表明,4种不同情形下决策类的最优近似更新的时间复杂度存在差异,与非增量式更新方法相比,所提最优近似粗糙集的增量式更新方法可行且高效.
Abstract:
The approximate representation and calculation of target concept on approximate space are the foundation of rough set model study. For dynamic attribute reduction and related tasks, it is often necessary to update the approximation of the target concept of rough set theory, and most existing updating approaches are based on the lower and upper approximations of target concepts. This paper presents a dynamic approach for updating the optimal set when a single object is added into the decision table. Aiming at the dynamic change of condition class and decision class caused by the incremental object, the paper discusses the updating mechanisms of optimal approximation set in four different cases and then the dynamic updating algorithm of optimal approximation set is obtained. Finally, the experiments on different data sets from UCI show that the time complexity of optimal approximate updating of decision classes are different for four different cases and the incremental methods are feasible and efficient for decision systems of added one single object.

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备注/Memo

备注/Memo:
Received:2020-10-29;Accepted:2021-01-03
Foundation:National Natural Science Foundation of China (61991401);Key Program of Natural Science Foundation of Jiangxi Province (20192ACBL20010)
Corresponding author:Associate professor LUO Laipeng. E-mail: luolp789@163.com
Citation:LUO Laipeng, FAN Zizhu. A dynamic approach for updating the optimal approximation of rough set[J]. Journal of Shenzhen University Science and Engineering, 2021, 38(3): 324-330.(in Chinese)
基金项目:国家自然科学基金资助项目(61991401);江西省自然科学基金重点资助项目(20192ACBL20010)
作者简介:罗来鹏(1973—),华东交通大学副教授.研究方向:粗糙集与粒计算.E-mail:luolp789@163.com
引文:罗来鹏,范自柱.粗糙集最优近似的动态更新方法[J]. 深圳大学学报理工版,2021,38(3):324-330.
更新日期/Last Update: 2021-05-30