  Automatic classification methods applied to sky surveys have revolutionized
the astronomical target selection process. Most surveys generate a vast amount
of time series, or \quotes{lightcurves}, that represent the brightness
variability of stellar objects in time. Unfortunately, lightcurves'
observations take several years to be completed, producing truncated time
series that generally remain without the application of automatic classifiers
until they are finished. This happens because state of the art methods rely on
a variety of statistical descriptors or features that present an increasing
degree of dispersion when the number of observations decreases, which reduces
their precision. In this paper we propose a novel method that increases the
performance of automatic classifiers of variable stars by incorporating the
deviations that scarcity of observations produces. Our method uses Gaussian
Process Regression to form a probabilistic model of each lightcurve's
observations. Then, based on this model, bootstrapped samples of the time
series features are generated. Finally a bagging approach is used to improve
the overall performance of the classification. We perform tests on the MACHO
and OGLE catalogs, results show that our method classifies effectively some
variability classes using a small fraction of the original observations. For
example, we found that RR Lyrae stars can be classified with around 80\% of
accuracy just by observing the first 5\% of the whole lightcurves' observations
in MACHO and OGLE catalogs. We believe these results prove that, when studying
lightcurves, it is important to consider the features' error and how the
measurement process impacts it.
