电子学报 ›› 2016, Vol. 44 ›› Issue (7): 1574-1580.DOI: 10.3969/j.issn.0372-2112.2016.07.008

• 学术论文 • 上一篇    下一篇

粗糙集近似集不确定性研究

张清华1,2, 薛玉斌1, 胡峰2, 于洪2   

  1. 1. 重庆邮电大学理学院, 重庆 400065;
    2. 重庆邮电大学计算智能重庆市重点实验室, 重庆 400065
  • 收稿日期:2014-12-17 修回日期:2015-03-22 出版日期:2016-07-25 发布日期:2016-07-25
  • 作者简介:张清华 男,1974年11月出生,重庆沙坪坝人.教授,硕士生导师.1998年、2003年和2009年分别在四川大学、重庆邮电大学和西南交通大学获理学学士、工学硕士和工学博士学位.现为中国人工智能学会粗糙集与软计算专委会秘书长,主要从事不确定信息处理、粗糙集与粒计算等方面的研究工作.E-mail:zhangqh@cqupt.edu.cn;薛玉斌 男,1990年5月出生,重庆潼南人.硕士研究生,研究方向为不确定信息处理、粗糙集与粒计算
  • 基金资助:

    国家自然科学基金(No.61472056,No.61309014,No.61379114);重庆市自然科学基金(No.cstc2012jjA40032,cstc2013jcyjA4006)

Research on Uncertainty of Approximation Set of Rough Set

ZHANG Qing-hua1,2, XUE Yu-bin1, HU Feng2, YU Hong2   

  1. 1. School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2014-12-17 Revised:2015-03-22 Online:2016-07-25 Published:2016-07-25

摘要:

粗糙集用上、下近似集刻画不确定目标集合,而粗糙集的近似集用0.5-近似集作为不确定目标集合的近似集.本文首先分析了基于粗糙集的0.5-近似集相似度的属性约简算法存在理论不完备的不足,指出这种相似度具有随知识粒度变化不敏感的缺陷.然后进一步给出了多粒度知识空间下相似度的变化规律,提出了粗糙集近似集的模糊度概念,分析了粗糙集近似集的模糊度在多粒度知识空间下的变化规律,进而提出了相应的属性约简算法.从新的视角构建了目标概念与其近似集的差异性度量方法.

关键词: 粗糙集, 近似集, 模糊度, 不确定性, 多粒度

Abstract:

Rough set describes an uncertain target set with upper and lower approximation sets,and approximation set of rough set uses 0.5-approximation set as an approximation set of the uncertain target set.In this paper,we firstly find that the theory of attribute reduction algorithm based on similarity between target set and its 0.5-approximation set is still incomplete,and this similarity is not sensitive to changing granularities.In order to overcome above shortcomings,the change rule of similarity with changing granularities in a multi-granularity space is analyzed,fuzzy degree of approximation set is defined,and the change rules of this fuzziness with changing granularities are analyzed in detail in a hierarchical space.Finally,a new attribute reduction algorithm is proposed.From a new perspective,a kind of differentiation measure between an uncertain target set and its approximation set is presented.

Key words: rough set, approximation set, fuzziness, uncertainty, multi-granularity

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