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In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative; in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual (in) discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced vaguely quantified rough set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued) data, and encapsulates the existing noise-tolerant data reduction approach using variable precision rough sets (VPRS), as well as the traditional rough set model, as special cases.