Due to the explosive growth of stored information worldwide, feature selection (FS) is becoming an increasingly important step, particularly given the abundance of noisy, irrelevant or misleading features. The main aim of FS is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original set of features. However, the problem of finding optimal reductions is challenging as there is always a trade-off between the extent of reduction and the resulting information loss. This topic has been of particular interest in rough and fuzzy-rough set theory, as these provide a mechanism for defining optimality using only the data itself. Evolutionary methods have been used to try to find rough and fuzzy-rough optimal reductions, but these approaches ignore the fact that not all equally-sized reducts have the same utility for classifiers. This paper presents a novel approach for fuzzy-rough feature selection that uses Estimation of Distribution Algorithms to maintain information about the quality of features, to then obtain a better quality reduct that is more useful in general.