知识系统中全粒度粗糙集及概念漂移的研究  

Study on Entire-Granulation Rough Sets and Concept Drifting in a Knowledge System

作　　者：邓大勇[1,2,3] 卢克文 苗夺谦[3] 黄厚宽[4] DENG Da-Yong;LU Ke -Wen;MIAO Duo-Qian;HUANG Hou-KuanCollege of Mathematics(College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004;Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321004;School of Electronics and Information Engineering, Tongji University, Shanghai 201804;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044)

机构地区：[1]浙江师范大学数理与信息工程学院,浙江金华321004 [2]浙江师范大学行知学院,浙江金华321004 [3]同济大学电子与信息工程学院,上海201804 [4]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机学报》2019年第1期85-97,共13页Chinese Journal of Computers

基　　金：国家自然科学基金项目(61473030;61572442;61203247;61273304;61573259;61472166);浙江省自然科学基金项目(LY15F020012)资助

摘　　要：概念漂移探测是数据流挖掘的一个研究重点,不确定性分析是粗糙集理论的研究核心之一.大数据、数据流中存在不确定变化和概念漂移现象,但是,除F-粗糙集外,几乎所有的粗糙集模型都是静态模型或半动态模型,专注于各种不确定性研究,难以处理不确定性变化,也难以探测概念漂移.结合量子计算、数据流、概念漂移和粗糙集、F-粗糙集的基本观点,以上、下近似为工具,定义了知识系统中的全粒度粗糙集和上、下近似概念漂移,上、下近似概念耦合等概念,探讨了全粒度粗糙集的性质,分析了知识系统内概念的全局变化.全粒度粗糙集继承了Pawlak粗糙集和F-粗糙集的基本思想,以上、下近似簇为工具表示了概念在知识系统内的各种可能变化.用嵌套哈斯图表示了概念不同情况下的同一性和差异性:同一层内的表示没有发生概念漂移,不同层内的表示发生了概念漂移.以正区域为工具,定义了决策表中的全粒度正区域和概念漂移、概念耦合等概念,探究了全粒度正区域的性质,分析了决策表内整体概念的全局变化.全粒度正区域表示了决策表中各种可能情况下的正区域,用嵌套哈斯图表示了正区域簇的同一性和差异性:同一层内没有发生相对于正区域的概念漂移,不同层内发生了相对于正区域的概念漂移.在全粒度粗糙集意义下,定义了全粒度绝对约简、全粒度值约简、全粒度Pawlak约简等属性约简,并探讨其性质.与大部分的属性约简不同(仅仅与并行约简和多粒度约简类似),全粒度属性约简要求概念的所有可能表示不发生概念漂移.进一步探讨了属性约简的优缺点,属性约简使得概念的表示变得单一,冗余属性的存在增加了概念表示的丰富性、多样性.在认识论方面,以粗糙集和粒计算为工具分析了人类认识世界的局部性与全局性,对人类认识世界的方式进行了进一步探�Concept drifting detection is one of the hot topics in data stream mining,and analysis of uncertainty is dominant in rough set theory.There exist the change of uncertainty and concept drifting in big data and data stream.However,except for F-rough sets,almost all of rough set models are static models or semi-dynamic models,which study on vagueness and uncertainty.It is hard for them to deal with the change of uncertainty,and to detect concept drifting.Combined with the ideas of quantum computing,data stream,concept drifting,rough sets and F-rough sets,a rough set model for entire granulations(called entire-granulation rough sets)is presented,and a lot of concepts,such as concept drifting of upper approximation,concept drifting of lower approximation,coupling of upper approximation and coupling of lower approximation,etc.are defined.The properties of entire-granulation rough sets are investigated,and the change of uncertainty for a concept in a knowledge system is analyzed with these definitions.Entire-granulation rough sets inherit the basic ideas of Pawlak rough sets and F-rough sets,which describe all of the changes of uncertainty for a concept with a family of upper approximations and lower approximations.Embedded Hasse diagram is employed to express the identity and diversity for a concept in different cases:There exists no concept drifting for the same level of concept expressions but exists concept drifting for the different levels of concept expressions.With the positive region,the positive region for entire granulations is defined,and concept drifting,concept coupling are defined in a decision system.The properties of entire-granulation positive region are discussed,and the analysis and measurement for the change of concept uncertainty are conducted.Entire-granulation positive region expresses all of the positive regions in various cases in a decision system.Embedded Hasse diagram is also employed to express the identity and diversity for the family of positive regions:There exists no concept drifting rel

关 键 词：全粒度粗糙集 概念漂移 偏序关系 概念耦合 上、下近似 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]