@ARTICLE { Rsds38738,
author = { Eric C. C., Tsang and  Bingzhen, Sun and  Weimin, Ma}, 
title = {General relation-based variable precision rough fuzzy set}, 
journal = {International Journal of Machine Learning and Cybernetics}, 
number = 	{(no. 3)}, 
pages = {891}, 
publisher = {Springer}, 
month = {06}, 
year = 2017, 
abstract = {AbstractIn order to effectively handle the real-valued data sets in practice, it is valuable from theoretical and practical aspects to combine fuzzy rough set and variable precision rough set so that a powerful tool can be developed. That is, the model of fuzzy variable precision rough set, which not only can handle numerical data but also is less sensitive to misclassification and perturbation,In this paper, we propose a new variable precision rough fuzzy set by introducing the variable precision parameter to generalized rough fuzzy set, i.e., the variable precision rough fuzzy set based on general relation. We, respectively, define the variable precision rough lower and upper approximations of any fuzzy set and it level set with variable precision parameter by constructive approach. Also, we present the properties of the proposed model in detail. Meanwhile, we establish the relationship between the variable precision rough approximation of a fuzzy set and the rough approximation of the level set for a fuzzy set. Furthermore, we give a new approach to uncertainty measure for variable precision rough fuzzy set established in this paper in order to overcome the limitations of the traditional methods. Finally, some numerical example are used to illuminate the validity of the conclusions given in this paper.}, 
keywords = {fuzzy sets, rough sets, conjunction, fuzzy rough sets, hybridization, implication, lower and upper approximation, rough set, 03G25, 06D15, Aggregation operators, Antitone involution, artificial intelligence, axiomatic approach, Basic algebra, beta-precision, classification, classification, Cloud model, Congruence regularity, }, 
}