We propose a novel feature selection (FS) method based on peculiar fuzzy set generation, aggregation, and ordering. In particular, here we propose to elicit the fuzzy membership by a proper probability-possibility transformation of frequencies stemming from the bootstrap application of different filter FS methods. At the same time, we aggregate such vague scores of each feature via the recently introduced SMART-or fuzzy aggregation operator. Finally, to rank the features for the selection proposal we adopt Yager’s ordering. Empirical results on benchmark databases show an overperformance of our approach with respect to different generation techniques, or aggregation functions and orderings.