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Lagrangian relaxation for SVM feature selection

M Gaudioso(manlio.gaudioso***at***unical.it)
E Gorgone(egorgone***at***ulb.ac.be)
M Labbé(mlabbe***at***ulb.ac.be)
A.M Rodriguez-Chia(antonio.rodriguezchia***at***uca.es)

Abstract: We discuss a Lagrangian-relaxation-based heuristics for dealing with feature selection in a standard L1 norm Support Vector Machine (SVM) framework for binary classification. The feature selection model we adopt is a Mixed Binary Linear Programming problem and it 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 characterized by a high number of features and a relatively small number of samples.

Keywords: SVM classification, feature selection, Lagrangian relaxation, nonsmooth optimization

Category 1: Integer Programming ((Mixed) Integer Linear Programming )

Citation: November 2015

Download: [PDF]

Entry Submitted: 11/18/2015
Entry Accepted: 11/18/2015
Entry Last Modified: 11/18/2015

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