Repositorio de producción científica de la Universidad de Sevilla

Multi-group support vector machines with measurement costs a biobjective approach

 

Advanced Search
 
Opened Access Multi-group support vector machines with measurement costs a biobjective approach
Cites

Show item statistics
Icon
Export to
Author: Carrizosa Priego, Emilio José
Martín Barragán, Belén
Romero Morales, María Dolores
Department: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Date: 2008-03
Published in: Discrete Applied Mathematics, 156 (6), 950-966.
Document type: Article
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 (modelled 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.
Cite: Carrizosa Priego, E.J., Martín Barragán, B. y Romero Morales, M.D. (2008). Multi-group support vector machines with measurement costs a biobjective approach. Discrete Applied Mathematics, 156 (6), 950-966.
Size: 495.0Kb
Format: PDF

URI: http://hdl.handle.net/11441/44825

DOI: 10.1016/j.dam.2007.05.060

See editor´s version

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

This item appears in the following Collection(s)