ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (11): 2607-2612.doi: 10.7544/issn1000-1239.2016.20150803

• 人工智能 • 上一篇    下一篇

信息表中概念漂移与不确定性分析

邓大勇1,2,4,苗夺谦2,黄厚宽3   

  1. 1(浙江师范大学数理与信息工程学院 浙江金华 321004); 2(同济大学电子与信息工程学院 上海 201804); 3(北京交通大学计算机与信息技术学院 北京 100044); 4(浙江师范大学行知学院 浙江金华 321004) (dayongd@163.com)
  • 出版日期: 2016-11-01
  • 基金资助: 
    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,LY13F020016) This work was supported by the National Natural Science Foundation of China (61473030,61572442,61203247,61273304,61573259,61472166) and the Natural Science Foundation of Zhejiang Province of China (LY15F020012,LY13F020016).

Analysis of Concept Drifting and Uncertainty in an Information Table

Deng Dayong1,2,4, Miao Duoqian2, Huang Houkuan3   

  1. 1(College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004); 2(School of Electronics and Information, Tongji University, Shanghai 201804); 3(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044); 4(Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321004)
  • Online: 2016-11-01

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. 结合数据流、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,定义了上下近似概念漂移、上下近似概念耦合等概念,据此分析了信息表内概念随着属性而变化的特点. 以正区域为工具,定义了决策表内概念漂移、概念耦合等概念,分析了决策表内整体概念随属性变化而变化. 在认识论方面,从理想和现实2方面定义了认识收敛, 从粒计算、粗糙集的角度对人类认识世界的方式进行了探讨.

关键词: 粗糙集, 概念漂移, 属性约简, 概念耦合, 上下近似

Abstract: Concept drifting detection is one of hot topics in data stream mining, and analysis of uncertainty is dominant in rough set theory. Combined with the ideas of data stream, concept drifting, rough sets and F-rough sets, a lot of concepts such as concept drifting of upper approximation, concept drifting of lower approximation, concept coupling of upper approximation and concept coupling of lower approximation etc are defined. The change of concepts in an information system is analyzed with these definitions. With the positive region, integral concept drifting, integral concept coupling are defined. The analysis and measurement for the change of concept uncertainty are conducted. From the view of epistemology, the concept of cognition convergence is defined from the ways of idealism and realism. It provides heuristic information for realizing the world of human beings from the viewpoints of granular computing and rough sets.

Key words: rough sets, concept drift, attribute reduction, concept coupling, upper and lower approximation

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