@INPROCEEDINGS { Rsds4554,
author = {Radzikowska, Anna Maria and De Cock, Martine and Cornelis, Chris}, 
title = {Vaguely Quantified 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 = {87--94}, 
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 = {The hybridization of rough sets and fuzzy sets has focused on creating an end product that extends both contributing computing paradigms in a conservative way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular, although they allow for gradual membership, fuzzy rough sets are still abrupt in a sense that adding or omitting a single element may drastically alter the outcome of the approximations. In this paper, we revisit the hybridization process by introducing vague quantifiers like â€?someâ€? or â€?mostâ€? into the definition of upper and lower approximation. The resulting vaguely quantified rough set (VQRS) model is closely related to Ziarko's variable precision rough set (VPRS) model.}, 
keywords = {vague quantifiers, fuzzy sets, rough sets, vprs model, }, 
}