Feature selection for multiclass discrimination via mixed-integer linear programming

Authors:
F. J. Iannarilli, Jr.;P. A. Rubin
Affiliations:
Aerodyne Res. Inc., Billerica, MA, USA;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2003

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Abstract

We reformulate branch-and-bound feature selection employing L∞ or particular Lp 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.