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属性约简准则与约简信息损失的研究
引用本文:邓大勇,薛欢欢,苗夺谦,卢克文.属性约简准则与约简信息损失的研究[J].电子学报,2017,45(2):401-407.
作者姓名:邓大勇  薛欢欢  苗夺谦  卢克文
作者单位: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
基金项目:国家自然科学基金,浙江省自然科学基金,浙江省自然科学青年基金
摘    要:属性约简是粗糙集的重要研究内容,信息熵是度量信息量的方法.在研究绝对约简和几种相对约简的基础上,归纳出属性约简的一般准则.定义了基于条件属性信息熵的属性约简和基于联合熵的属性约简,研究了几种属性约简与绝对约简之间的关系.定义了基于条件属性信息熵的约简信息损失,澄清了属性约简不损失信息的含糊观念,指出了属性约简只是在约简准则意义下不损失信息,在信息熵意义下可能损失信息.为进一步研究粗糙集、粒计算中属性约简与分类夯实了信息论基础.

关 键 词:粗糙集  属性约简  信息熵  联合熵  信息损失  
收稿时间:2016-03-21

Study on Criteria of Attribute Reduction and Information Loss of Attribute Reduction
DENG Da-yong,XUE Huan-huan,MIAO Duo-qian,LU Ke-wen.Study on Criteria of Attribute Reduction and Information Loss of Attribute Reduction[J].Acta Electronica Sinica,2017,45(2):401-407.
Authors:DENG Da-yong  XUE Huan-huan  MIAO Duo-qian  LU Ke-wen
Affiliation:1. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, China; 2. Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321004, China; 3. School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Abstract:Attribute reduction is one of important topics in rough set theory,and information entropy is an index of measuring the amount of information.After investigating absolute attribute reduct and several kinds of relatively attribute reducts,a general criterion of reducts is induced in rough set theory.With this criterion of reducts,attribute reduct based on information entropy and attribute reduct based on joint entropy are defined.The relationships among attribute reducts and absolute attribute reduct are investigated.Moreover,information loss based on information entropy for attribute reducts is defined,which can measure information loss after attribute reduction has been conducted.The old concepts that attribute reduction can not lose information are improved,and attribute reduction and classification can be further investigated from information loss and information entropy.
Keywords:rough sets  attribute reduction  information entropy  joint entropy  information loss
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