  Variable stars of RR Lyrae type are a prime tool to obtain distances to old
stellar populations in the Milky Way, and one of the main aims of the Vista
Variables in the Via Lactea (VVV) near-infrared survey is to use them to map
the structure of the Galactic Bulge. Due to the large number of expected
sources, this requires an automated mechanism for selecting RR Lyrae,and
particularly those of the more easily recognized type ab (i.e.,
fundamental-mode pulsators), from the 10^6-10^7 variables expected in the VVV
survey area. In this work we describe a supervised machine-learned classifier
constructed for assigning a score to a K_s-band VVV light curve that indicates
its likelihood of being ab-type RR Lyrae. We describe the key steps in the
construction of the classifier, which were the choice of features, training
set, selection of aperture and family of classifiers. We find that the AdaBoost
family of classifiers give consistently the best performance for our problem,
and obtain a classifier based on the AdaBoost algorithm that achieves a
harmonic mean between false positives and false negatives of ~7% for typical
VVV light curve sets. This performance is estimated using cross-validation and
through the comparison to two independent datasets that were classified by
human experts.
