  The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main
goals of the VVV(X) surveys. The overwhelming number of sources under analysis
request the use of automatic procedures. In this context, previous works
introduced the use of Machine Learning (ML) methods for the variable star
classification. Our goal is the development and analysis of an automatic
procedure, based on ML, for the identification of RRLs in the VVV Survey. This
procedure will be use to generate reliable catalogs integrated over several
tiles in the survey. After the reconstruction of light-curves, we extract a set
of period and intensity-based features. We use for the first time a new subset
of pseudo color features. We discuss all the appropriate steps needed to define
our automatic pipeline: selection of quality measures; sampling procedures;
classifier setup and model selection. As final result, we construct an ensemble
classifier with an average Recall of 0.48 and average Precision of 0.86 over 15
tiles. We also make available our processed datasets and a catalog of candidate
RRLs. Perhaps most interestingly, from a classification perspective based on
photometric broad-band data, is that our results indicate that Color is an
informative feature type of the RRL that should be considered for automatic
classification methods via ML. We also argue that Recall and Precision in both
tables and curves are high quality metrics for this highly imbalanced problem.
Furthermore, we show for our VVV data-set that to have good estimates it is
important to use the original distribution more than reduced samples with an
artificial balance. Finally, we show that the use of ensemble classifiers helps
resolve the crucial model selection step, and that most errors in the
identification of RRLs are related to low quality observations of some sources
or to the difficulty to resolve the RRL-C type given the date.
