RT journal article
T1 Lagrangian relaxation for SVM feature selection
A1 Gaudioso, Manlio
A1 Gorgone, Enrico
A1 Labbé, Martine
A1 Rodríguez Chía, Antonio Manuel
A2 Estadística e Investigación Operativa
K1 SVM classification
K1 feature selection
K1 Lagrangian relaxatio
K1 nonsmooth optimization
AB We discuss a Lagrangian-relaxation-based heuristics for dealing with feature selection in the Support Vector Machine (SVM) framework for binary classification. In particular we embed into our objective function a weighted combination of the L1 and L0 norm of the normal to the separating hyperplane. We come out with a Mixed Binary Linear Programming problem which is suitable for a Lagrangian relaxation approach. Based on a property of the optimal multiplier setting, we apply a consolidated nonsmooth optimization ascent algorithm to solve the resulting Lagrangian dual. In the proposed approach we get, at every ascent step, both a lower bound on the optimal solution as well as a feasible solution at low computational cost. We present the results of our numerical experiments on some benchmark datasets.
PB Elsevier Ltd
SN 0305-0548
YR 2017
FD 2017
LK http://hdl.handle.net/10498/33860
UL http://hdl.handle.net/10498/33860
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026