@misc{10498/33860,
year = {2017},
url = {http://hdl.handle.net/10498/33860},
abstract = {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.},
publisher = {Elsevier Ltd},
keywords = {SVM classification},
keywords = {feature selection},
keywords = {Lagrangian relaxatio},
keywords = {nonsmooth optimization},
title = {Lagrangian relaxation for SVM feature selection},
doi = {10.1016/J.COR.2017.06.001},
author = {Gaudioso, Manlio and Gorgone, Enrico and Labbé, Martine and Rodríguez Chía, Antonio Manuel},
}