  Time domain radio astronomy observing campaigns frequently generate large
volumes of data. Our goal is to develop automated methods that can identify
events of interest buried within the larger data stream. The V-FASTR fast
transient system was designed to detect rare fast radio bursts (FRBs) within
data collected by the Very Long Baseline Array. The resulting event candidates
constitute a significant burden in terms of subsequent human reviewing time. We
have trained and deployed a machine learning classifier that marks each
candidate detection as a pulse from a known pulsar, an artifact due to radio
frequency interference, or a potential new discovery. The classifier maintains
high reliability by restricting its predictions to those with at least 90%
confidence. We have also implemented several efficiency and usability
improvements to the V-FASTR web-based candidate review system. Overall, we
found that time spent reviewing decreased and the fraction of interesting
candidates increased. The classifier now classifies (and therefore filters)
80-90% of the candidates, with an accuracy greater than 98%, leaving only the
10-20% most promising candidates to be reviewed by humans.
