  The need for the development of automatic tools to explore astronomical
databases has been recognized since the inception of CCDs and modern computers.
Astronomers already have developed solutions to tackle several science
problems, such as automatic classification of stellar objects, outlier
detection, and globular clusters identification, among others. New science
problems emerge and it is critical to be able to re-use the models learned
before, without rebuilding everything from the beginning when the science
problem changes. In this paper, we propose a new meta-model that automatically
integrates existing classification models of variable stars. The proposed
meta-model incorporates existing models that are trained in a different
context, answering different questions and using different representations of
data. Conventional mixture of experts algorithms in machine learning literature
can not be used since each expert (model) uses different inputs. We also
consider computational complexity of the model by using the most expensive
models only when it is necessary. We test our model with EROS-2 and MACHO
datasets, and we show that we solve most of the classification challenges only
by training a meta-model to learn how to integrate the previous experts.
