  With the advent of Big Data era, data reduction methods are highly demanded
given its ability to simplify huge data, and ease complex learning processes.
Concretely, algorithms that are able to filter relevant dimensions from a set
of millions are of huge importance. Although effective, these techniques suffer
from the "scalability" curse as well.
  In this work, we propose a distributed feature weighting algorithm, which is
able to rank millions of features in parallel using large samples. This method,
inspired by the well-known RELIEF algorithm, introduces a novel redundancy
elimination measure that provides similar schemes to those based on entropy at
a much lower cost. It also allows smooth scale up when more instances are
demanded in feature estimations. Empirical tests performed on our method show
its estimation ability in manifold huge sets --both in number of features and
instances--, as well as its simplified runtime cost (specially, at the
redundancy detection step).
