属性集变化条件下集值决策信息系统的增量属性约简方法  

Incremental attribute reduction method for set-valued decision information system with variable attribute sets

作　　者：刘超 王磊[1,2] 杨文[1,2] 钟强强[1,2] 黎敏 LIU Chao;WANG Lei;YANG Wen;ZHONG Qiangqiang;LI Min(School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing(Nanchang Institute of Technology),Nanchang 330099,China)

机构地区：[1]南昌工程学院信息工程学院,南昌330099 [2]江西省水信息协同感知与智能处理重点实验室(南昌工程学院),南昌330099

出　　处：《计算机应用》2022年第2期463-468,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61562061);江西省教育厅科技项目(GJJ170995)。

摘　　要：为了解决集值决策信息系统中的属性数量不断发生动态变化时,静态属性约简方法无法高效更新属性约简的问题,提出一种以知识粒度为启发信息的增量式属性约简方法。首先,介绍集值决策信息系统的相关概念,接着介绍知识粒度的定义并将其矩阵表示方法推广到此系统中;然后,分析增量式约简的更新机制,并基于知识粒度设计了增量式属性约简方法;最后,选取了3个不同的数据集进行实验。当3个数据集的属性数由20%增加到100%时,传统的非增量式方法的约简耗时分别为54.84 s、108.01 s、565.93 s,增量式方法的约简耗时分别为7.57 s、4.85 s、50.39 s。实验结果表明,在不影响属性约简精度的前提下,所提出的增量式方法比非增量式方法更加快速。In order to solve the problem that static attribute reduction cannot update attribute reduction efficiently when the number of attributes in the set-valued decision information system changes continuously,an incremental attribute reduction method with knowledge granularity as heuristic information was proposed.Firstly,the related concepts of the set-valued decision information system were introduced,then the definition of knowledge granularity was introduced,and its matrix representation method was extended to this system.Secondly,the update mechanism of incremental reduction was analyzed,and an incremental attribute reduction method was designed on the basis of knowledge granularity.Finally,three different datasets were selected for the experiments.When the number of attributes of the three datasets increased from 20%to 100%,the reduction time of the traditional non-incremental method was 54.84 s,108.01 s,and 565.93 s respectively,and the reduction time of the incremental method was 7.57 s,4.85 s,and 50.39 s respectively.Experimental results demonstrate that the proposed incremental method is more faster than the non-incremental method under the condition that the accuracy of attribute reduction is not affected.

关 键 词：粗糙集理论 集值决策信息系统 知识粒度 属性约简 增量学习 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]