  Multi-class classification algorithms are very widely used, but we argue that
they are not always ideal from a theoretical perspective, because they assume
all classes are characterized by the data, whereas in many applications,
training data for some classes may be entirely absent, rare, or statistically
unrepresentative. We evaluate one-sided classifiers as an alternative, since
they assume that only one class (the target) is well characterized. We consider
a task of identifying whether a substance contains a chlorinated solvent, based
on its chemical spectrum. For this application, it is not really feasible to
collect a statistically representative set of outliers, since that group may
contain \emph{anything} apart from the target chlorinated solvents. Using a new
one-sided classification toolkit, we compare a One-Sided k-NN algorithm with
two well-known binary classification algorithms, and conclude that the
one-sided classifier is more robust to unexpected outliers.
