  Searches for millisecond-duration, dispersed single pulses have become a
standard tool used during radio pulsar surveys in the last decade. They have
enabled the discovery of two new classes of sources: rotating radio transients
and fast radio bursts. However, we are now in a regime where the sensitivity to
single pulses in radio surveys is often limited more by the strong background
of radio frequency interference (RFI, which can greatly increase the
false-positive rate) than by the sensitivity of the telescope itself. To
mitigate this problem, we introduce the Single-pulse Searcher (SpS). This is a
new machine-learning classifier designed to identify astrophysical signals in a
strong RFI environment, and optimized to process the large data volumes
produced by the new generation of aperture array telescopes. It has been
specifically developed for the LOFAR Tied-Array All-Sky Survey (LOTAAS), an
ongoing survey for pulsars and fast radio transients in the northern
hemisphere. During its development, SpS discovered 7 new pulsars and blindly
identified ~80 known sources. The modular design of the software offers the
possibility to easily adapt it to other studies with different instruments and
characteristics. Indeed, SpS has already been used in other projects, e.g. to
identify pulses from the fast radio burst source FRB 121102. The software
development is complete and SpS is now being used to re-process all LOTAAS data
collected to date.
