Feature selection for multiclass discrimination via mixed-integer linear programming

We reformulate branch-and-bound feature selection employing L/sub (infinity)// or particular L/sub p/ metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings.