  We have investigated and applied machine-learning algorithms for Infrared
Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the
GLIMPSE catalogue of the infrared objects in the Galactic plane can be
classified into different stellar populations based on the colours inferred
from their broadband photometric magnitudes ($J$, $H$ and $K_s$ from 2MASS, and
the four \textit{Spitzer}/IRAC bands). The algorithms tested in this pilot
study are variants of the $k$-Nearest Neighbours ($k$-NN) approach, which is
ideal for exploratory studies of classification problems where interrelations
between variables and classes are complicated. The aims of this study are (1)
to provide an automated tool to select reliable WR candidates and potentially
other classes of objects, (2) to measure the efficiency of infrared colour
selection at performing these tasks and, (3) to lay the groundwork for
statistically inferring the total number of WR stars in our Galaxy. We report
the performance results obtained over a set of known objects and selected
candidates for which we have carried out follow-up spectroscopic observations,
and confirm the discovery of 4 new WR stars.
