  Over the past decade wide-field optical time-domain surveys have increased
the discovery rate of transients to the point that $\lesssim 10\%$ are being
spectroscopically classified. Despite this, these surveys have enabled the
discovery of new and rare types of transients, most notably the class of
hydrogen-poor superluminous supernovae (SLSN-I), with about 150 events
confirmed to date. Here we present a machine-learning classification algorithm
targeted at rapid identification of a pure sample of SLSN-I to enable
spectroscopic and multi-wavelength follow-up. This algorithm is part of the
FLEET (Finding Luminous and Exotic Extragalactic Transients) observational
strategy. It utilizes both light curve and contextual information, but without
the need for a redshift, to assign each newly-discovered transient a
probability of being a SLSN-I. This classifier can achieve a maximum purity of
about 85\% (with 20\% completeness) when observing a selection of SLSN-I
candidates. Additionally, we present two alternative classifiers that use
either redshifts or complete light curves and can achieve an even higher purity
and completeness. At the current discovery rate, the FLEET algorithm can
provide about $20$ SLSN-I candidates per year for spectroscopic follow-up with
85\% purity; with the Legacy Survey of Space and Time we anticipate this will
rise to more than $\sim 10^3$ events per year.
