A new method for measuring uncertainty and fuzziness in rough set theory

被引：346

作者：

Liang, JY ^{[1
]}

Chin, KS

Dang, CY

Yam, RCM

机构：

[1] Shanxi Univ, Dept Comp Sci, Taiyuan 030006, Peoples R China

[2] City Univ Hong Kong, Dept Mfg Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF GENERAL SYSTEMS | 2002年 / 31卷 / 04期

基金：

中国国家自然科学基金; 上海市自然科学基金;

关键词：

rough sets; information entropy; uncertainty; fuzziness; rough classification; data analysis;

D O I：

10.1080/0308107021000013635

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannon's entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.

引用

页码：331 / 342

页数：12

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