Feature selection aims to find the most important
information from a given set of features. As this task can be
seen as an optimization problem, the combinatorial growth of
the possible solutions may be in-viable for a exhaustive search.
In this paper we propose a new nature-inspired feature selection
technique based on the bats behaviour, which has never been
applied to this context so far. The wrapper approach combines
the power of exploration of the bats together with the speed of
the Optimum-Path Forest classifier to find the set of features
that maximizes the accuracy in a validating set. Experiments
conducted in five public datasets have demonstrated that the
proposed approach can outperform some well-known swarm based
techniques.