In previous work we proposed an algorithm, REFER, for removing logically redundant features in a dataset consisting of Boolean examples, each labelled with one of any number of possible class labels. We define redundant features as those which can be removed without compromising the learning of a classification rule. Such redundant features are said to be covered by another present feature. Disjoint subsets of examples of the same class, called neighbourhoods, are used to both permit feature reduction in multi-class problems and to enable more features to be removed. A further benefit of using neighbourhoods is that redundant features can be detected as a result of being covered by a combination of features. In this paper we review the REFER method, demonstrate how this effect comes about, and discuss adaptations which take advantage of this effect.