  We describe a multivariate classifier for candidate events in a templated
search for gravitational-wave (GW) inspiral signals from
neutron-star--black-hole (NS-BH) binaries, in data from ground-based detectors
where sensitivity is limited by non-Gaussian noise transients. The standard
signal-to-noise ratio (SNR) and chi-squared test for inspiral searches use only
properties of a single matched filter at the time of an event; instead, we
propose a classifier using features derived from a bank of inspiral templates
around the time of each event, and also from a search using approximate
sine-Gaussian templates. The classifier thus extracts additional information
from strain data to discriminate inspiral signals from noise transients. We
evaluate a Random Forest classifier on a set of single-detector events obtained
from realistic simulated advanced LIGO data, using simulated NS-BH signals
added to the data. The new classifier detects a factor of 1.5 -- 2 more signals
at low false positive rates as compared to the standard 're-weighted SNR'
statistic, and does not require the chi-squared test to be computed.
Conversely, if only the SNR and chi-squared values of single-detector events
are available, Random Forest classification performs nearly identically to the
re-weighted SNR.
