  The NASA's Transiting Exoplanet Survey Satellite (TESS) is about to provide
full-frame images of almost the entire sky. The amount of stellar data to be
analysed represents hundreds of millions stars, which is several orders of
magnitude above the amount of stars observed by CoRoT, Kepler, or K2 missions.
We aim at automatically classifying the newly observed stars, with near
real-time algorithms, to better guide their subsequent detailed studies. In
this paper, we present a classification algorithm built to recognise solar-like
pulsators among classical pulsators, which relies on the global amount of power
contained in the PSD, also known as the FliPer (Flicker in spectral Power
density). As each type of pulsating star has a characteristic background or
pulsation pattern, the shape of the PSD at different frequencies can be used to
characterise the type of pulsating star. The FliPer Classifier
(FliPer$_{Class}$) uses different FliPer parameters along with the effective
temperature as input parameters to feed a machine learning algorithm in order
to automatically classify the pulsating stars observed by TESS. Using noisy
TESS simulated data from the TESS Asteroseismic Science Consortium (TASC), we
manage to classify pulsators with a 98% accuracy. Among them, solar-like
pulsating stars are recognised with a 99% accuracy, which is of great interest
for further seismic analysis of these stars like our Sun. Similar results are
obtained when training our classifier and applying it to 27 days subsets of
real Kepler data. FliPer$_{Class}$ is part of the large TASC classification
pipeline developed by the TESS Data for Asteroseismology (T'DA) classification
working group.
