@INPROCEEDINGS { Rsds4551,
author = {Feng, Tao and Zhao, Hui-Yin and Li, Xiu-Min and Mi, Ju-Sheng}, 
title = {Information-Theoretic Measure of Uncertainty in Generalized Fuzzy Rough Sets}, 
booktitle = {Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'2007)}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 4482, 
pages = {63--70}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2007, 
editor = {An, A. and Stefanowski, Jerzy and Ramanna, Sheela and Butz, C. and Pedrycz, Witold and Wang, Guoying}, 
isbn = {978-3-540-72529-9}, 
abstract = {Rough set theory has become well-established as a mechanism for uncertainty management in a wide variety of applications. This paper studies the measurement of uncertainty in generalized fuzzy rough sets determined by a triangular norm. Based on information theory, the entropy of a generalized fuzzy approximation space is introduced, which is similar to Shannon's entropy. To measure uncertainty in generalized fuzzy rough sets, a notion of fuzziness is introduced. Some basic properties of this measure are examined. For a special triangular norm T=min , it is proved that the measure of fuzziness of a generalized fuzzy rough set is equal to zero if and only if the set is crisp and definable.}, 
keywords = {approximation operators, fuzzy sets, fuzzy rough sets, triangular norm, uncertainty, }, 
}