A Wrapper Approach for Feature Selection based on

13 Abstract 14 Besides optimizing classier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classication model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the tness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically signicant more compact sets and, in some cases, it can indeed improve the classication eectiveness.