  Remnant radio galaxies represent the dying phase of radio-loud active
galactic nuclei (AGN). Large samples of remnant radio galaxies are important
for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can
be identified in radio sky surveys based on their spectral index, or,
complementary, through visual inspection based on their radio morphology.
However, this is extremely time-consuming when applied to the new large and
sensitive radio surveys. Here we aim to reduce the amount of visual inspection
required to find AGN remnants based on their morphology, through supervised
machine learning trained on an existing sample of remnant candidates. For a
dataset of 4107 radio sources, with angular sizes larger than 60 arcsec, from
the LOw Frequency ARray (LOFAR) Two-Metre Sky Survey second data release
(LoTSS-DR2), we started with 151 radio sources that were visually classified as
'AGN remnant candidate'. We derived a wide range of morphological features for
all radio sources from their corresponding Stokes-I images: from simple source
catalogue-derived properties, to clustered Haralick-features, and
self-organising map (SOM) derived morphological features. We trained a random
forest classifier to separate the 'AGN remnant candidates' from the not yet
inspected sources. The SOM-derived features and the total to peak flux ratio of
a source are shown to be most salient to the classifier. We estimate that
$31\pm5\%$ of sources with positive predictions from our classifier will be
labelled 'AGN remnant candidates' upon visual inspection, while we estimate the
upper bound of the $95\%$ confidence interval for 'AGN remnant candidates' in
the negative predictions at $8\%$. Visual inspection of just the positive
predictions reduces the number of radio sources requiring visual inspection by
$73\%$.

Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics