  Improving survey specifications are causing an exponential rise in pulsar
candidate numbers and data volumes. We study the candidate filters used to
mitigate these problems during the past fifty years. We find that some existing
methods such as applying constraints on the total number of candidates
collected per observation, may have detrimental effects on the success of
pulsar searches. Those methods immune to such effects are found to be
ill-equipped to deal with the problems associated with increasing data volumes
and candidate numbers, motivating the development of new approaches. We
therefore present a new method designed for on-line operation. It selects
promising candidates using a purpose-built tree-based machine learning
classifier, the Gaussian Hellinger Very Fast Decision Tree (GH-VFDT), and a new
set of features for describing candidates. The features have been chosen so as
to i) maximise the separation between candidates arising from noise and those
of probable astrophysical origin, and ii) be as survey-independent as possible.
Using these features our new approach can process millions of candidates in
seconds (~1 million every 15 seconds), with high levels of pulsar recall
(90%+). This technique is therefore applicable to the large volumes of data
expected to be produced by the Square Kilometre Array (SKA). Use of this
approach has assisted in the discovery of 20 new pulsars in data obtained
during the LOFAR Tied-Array All-Sky Survey (LOTAAS).
