One of the weaknesses of classical (fuzzy) rough sets is their sensitivity to
noise, which is particularly undesirable for machine learning applications. One
approach to solve this issue is by making use of fuzzy quantifiers, as done by
the vaguely quantified fuzzy rough set (VQFRS) model. While this idea is
intuitive, the VQFRS model suffers from both theoretical flaws as well as from
suboptimal performance in applications. In this paper, we improve on VQFRS by
introducing fuzzy quantifier-based fuzzy rough sets (FQFRS), an intuitive
generalization of fuzzy rough sets that makes use of general unary and binary
quantification models. We show how several existing models fit in this
generalization as well as how it inspires novel ones. Several binary
quantification models are proposed to be used with FQFRS. We conduct a
theoretical study of their properties, and investigate their potential by
applying them to classification problems. In particular, we highlight Yager's
Weighted Implication-based (YWI) binary quantification model, which induces a
fuzzy rough set model that is both a significant improvement on VQFRS, as well
as a worthy competitor to the popular ordered weighted averaging based fuzzy
rough set (OWAFRS) model.

本文改进了模糊量化模糊粗糙集（VQFRS）模型，引入了基于模糊量词的模糊粗糙集（FQFRS）模型，使用泛化的一元和二元量化模型，并应用多个二元量化模型来分类，特别地，介绍了 Yager 的基于加权蕴涵的二元量化模型在 FQFRS 中的优化作用。