Paper Info
Title
Studies on Fuzzy Information Measures
Abstract
Fuzzy information measures play an important part in measuring the similarity degree between two pattern vectors in fuzzy circumstance. In this paper, two new fuzzy information measures are set up. Firstly, the classical similarity measures, such as dissimilarity measure (DM) and similarity measure (SM) are studied, an axiom theory about fuzzy entropy is surveyed, and all kinds of definitions of fuzzy entropy are discussed. Secondly, based on the idea of Shannon information entropy, two concepts of fuzzy joint entropy and fuzzy conditional entropy are proposed and the basic properties of them are given and proved. At last, two new measures, fuzzy absolute information measure (FAIM) and fuzzy relative information measure (FRIM), are set up, which can be used to measure the similarity degree between a fuzzy set A and a fuzzy set B. So, It provides a new research approach for studies on pattern similarity measure.
Year
DOI
Venue
2006
10.1109/FSKD.2007.534
fuzzy systems and knowledge discovery
Keywords
DocType
Volume
fuzzy circumstance,fuzzy information measures,fuzzy set theory,fuzzy set,fuzzy entropy,fuzzy set a,similarity degree,fuzzy joint entropy,shannon information entropy,fuzzy information measure,fuzzy relative information measure,entropy,fuzzy conditional entropy,fuzzy absolute information measure,fuzzy sets,random variables,delta modulation,agricultural engineering,probability distribution,conditional entropy,samarium,pattern recognition,information processing,information entropy
Conference
3
ISBN
Citations 
PageRank 
978-0-7695-2874-8
4
0.40
References 
Authors
7
4
Name
Order
Citations
PageRank
Shifei Ding1107494.63
Zhongzhi Shi22440238.03
Shixiong Xia310213.28
Fengxiang Jin412410.72