  Gravitational microlensing method is a powerful method to detect isolated
black holes in the Milky Way. During a microlensing event brightness of the
source increases and this feature is used by many photometric surveys to alert
on potential events. A typical microlensing event shows a characteristic light
curve, however, some outbursting variable stars may show similar light curves
to microlensing events especially when the cadence of observations is not dense
enough. Our aim is to device a method for distinguishing candidates for
microlensing events from any other types of alerts using solely their archival
photometric multi-wavelength data. The most common contaminants in the
microlensing event search are Classical Be-type stars, Young Stellar Objects
and Asymptotic Giant Branch stars such as Miras. We build a training set using
thousands of examples for the main classes of alerting stars combining optical
to mid-infrared magnitudes from Gaia, 2MASS and AllWISE catalogues. We used
supervised machine learning techniques to build models for classification of
alerts. We verified our method on 120 microlensing events reported by Gaia
Science Alerts which were studied spectroscopically and photometrically. With
the use of only archival information at 90% probability threshold we correctly
identified one-third of the microlensing events. We also run our classifier on
positions of 368 Gaia alerts which were flagged as potential candidates for
microlensing events. At the 90% probability threshold we classified 38
microlensing events and 29 other types of variables. The machine learning
supported method we developed can be universally used for current and future
alerting surveys in order to quickly assess the classification of galactic
transients and help decide on further follow-up observations.
