  A set of points $X = X_B \cup X_R \subseteq \mathbb{R}^d$ is linearly
separable if the convex hulls of $X_B$ and $X_R$ are disjoint, hence there
exists a hyperplane separating $X_B$ from $X_R$. Such a hyperplane provides a
method for classifying new points, according to which side of the hyperplane
the new points lie. When such a linear separation is not possible, it may still
be possible to partition $X_B$ and $X_R$ into prespecified numbers of groups,
in such a way that every group from $X_B$ is linearly separable from every
group from $X_R$. We may also discard some points as outliers, and seek to
minimize the number of outliers necessary to find such a partition. Based on
these ideas, Bertsimas and Shioda proposed the classification and regression by
integer optimization (CRIO) method in 2007. In this work we explore the integer
programming aspects of the classification part of CRIO, in particular
theoretical properties of the associated formulation. We are able to find
facet-inducing inequalities coming from the stable set polytope, hence showing
that this classification problem has exploitable combinatorial properties.
