  The recently launched NASA Transiting Exoplanet Survey Satellite (TESS)
mission is going to collect lightcurves for a few hundred million of stars and
we expect to increase the number of pulsating stars to analyze compared to the
few thousand stars observed by the CoRoT, $\textit{Kepler}$ and K2 missions.
However, most of the TESS targets have not yet been properly classified and
characterized. In order to improve the analysis of the TESS data, it is crucial
to determine the type of stellar pulsations in a timely manner. We propose an
automatic method to classify stars attending to their pulsation properties, in
particular, to identify solar-like pulsators among all TESS targets. It relies
on the use of the global amount of power contained in the power spectrum
(already known as the FliPer method) as a key parameter, along with the
effective temperature, to feed into a machine learning classifier. Our study,
based on TESS simulated datasets, shows that we are able to classify pulsators
with a $98\%$ accuracy.
