  Tens of millions of new variable objects are expected to be identified in
over a billion time series from the Gaia mission. Crossmatching known variable
sources with those from Gaia is crucial to incorporate current knowledge,
understand how these objects appear in the Gaia data, train supervised
classifiers to recognise known classes, and validate the results of the
Variability Processing and Analysis Coordination Unit (CU7) within the Gaia
Data Analysis and Processing Consortium (DPAC). The method employed by CU7 to
crossmatch variables for the first Gaia data release includes a binary
classifier to take into account positional uncertainties, proper motion,
targeted variability signals, and artefacts present in the early calibration of
the Gaia data. Crossmatching with a classifier makes it possible to automate
all those decisions which are typically made during visual inspection. The
classifier can be trained with objects characterized by a variety of attributes
to ensure similarity in multiple dimensions (astrometry, photometry,
time-series features), with no need for a-priori transformations to compare
different photometric bands, or of predictive models of the motion of objects
to compare positions. Other advantages as well as some disadvantages of the
method are discussed. Implementation steps from the training to the assessment
of the crossmatch classifier and selection of results are described.
