TY - JOUR
T1 - Multi-group Support Vector Machines with Measurement Costs
T2 - A Biobjective Approach
AU - Carrizosa, Emilio
AU - Martín-Barragán, Belén
AU - Romero Morales, Dolores
PY - 2008
Y1 - 2008
N2 - Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modeled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. We introduce a Biobjective Mixed Integer Problem, for which Pareto optimal solutions are obtained. Those Pareto optimal solutions correspond to different classification rules, among which the user would choose the one yielding the most appropriate compromise between the cost and the expected misclassification rate.
AB - Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modeled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. We introduce a Biobjective Mixed Integer Problem, for which Pareto optimal solutions are obtained. Those Pareto optimal solutions correspond to different classification rules, among which the user would choose the one yielding the most appropriate compromise between the cost and the expected misclassification rate.
KW - Multi-group classification
KW - Pareto optimality
KW - Biobjective Mixed Integer Programming
KW - Feature cost
KW - Support Vector Machines
U2 - 10.1016/j.dam.2007.05.060
DO - 10.1016/j.dam.2007.05.060
M3 - Journal article
SN - 0166-218X
VL - 156
SP - 950
EP - 966
JO - Discrete Applied Mathematics
JF - Discrete Applied Mathematics
IS - 6
ER -