Title of article :
Multi-group support vector machines with measurement costs: A biobjective approach Original Research Article
Author/Authors :
Emilio Carrizosa، نويسنده , , Belen Martin-Barragan، نويسنده , , Dolores Romero-Morales، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Pages :
17
From page :
950
To page :
966
Abstract :
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.
Keywords :
Multi-group classification , Pareto optimality , Feature cost , support vector machines , Biobjective Mixed Integer Programming
Journal title :
Discrete Applied Mathematics
Serial Year :
2008
Journal title :
Discrete Applied Mathematics
Record number :
886708
Link To Document :
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