%0 Journal Article 
%A Gaudioso, Manlio
%A Gorgone, Enrico
%A Labbé, Martine
%A Rodríguez Chía, Antonio Manuel
%T Lagrangian relaxation for SVM feature selection
%D 2017
%@ 0305-0548
%U http://hdl.handle.net/10498/33860
%X 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.
%K SVM classification
%K feature selection
%K Lagrangian relaxatio
%K nonsmooth optimization
%~ Universidad de Cádiz