RT journal article
T1 Mixed integer linear programming for feature selection in support vector machine
A1 Labbé, Martine
A1 Martínez Merino, Luisa Isabel
A1 Rodríguez Chía, Antonio Manuel
A2 Estadística e Investigación Operativa
K1 Mathematical programming
K1 Kernel search algorithm
K1 Supervised classification
K1 Support vector machine
K1 Feature selection
AB This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. Wepropose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.
PB Elsevier
SN 0166-218X
YR 2019
FD 2019
LK http://hdl.handle.net/10498/31357
UL http://hdl.handle.net/10498/31357
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026