Lagrangian relaxation for SVM feature selection

DOI10.1016/j.cor.2017.06.001zbMath1391.90430OpenAlexW2626153413MaRDI QIDQ1652409

Martine Labbé, Manlio Gaudioso, Enrico Gorgone, Antonio M. Rodríguez-Chía

Publication date: 11 July 2018

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01666453/file/Gaudiosoetal-preprint.pdf

zbMATH Keywords

nonsmooth optimizationLagrangian relaxationfeature selectionSVM classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Mixed integer programming (90C11) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (15)

On $\ell_p$-Support Vector Machines and Multidimensional Kernels ⋮ Feature selection using stochastic approximation with Barzilai and Borwein non-monotone gains ⋮ Generalizations, formulations and subgradient based heuristic with dynamic programming procedure for target set selection problems ⋮ Mixed integer linear programming for feature selection in support vector machine ⋮ Tightening big Ms in integer programming formulations for support vector machines with ramp loss ⋮ Robust optimal classification trees under noisy labels ⋮ Sparse optimization via vector \(k\)-norm and DC programming with an application to feature selection for support vector machines ⋮ Comparing two SVM models through different metrics based on the confusion matrix ⋮ Ellipsoidal classification via semidefinite programming ⋮ A comparative study of the leading machine learning techniques and two new optimization algorithms ⋮ Nonlinear optimization and support vector machines ⋮ Nonlinear optimization and support vector machines ⋮ Optimization problems for machine learning: a survey ⋮ Feature selection in SVM via polyhedral \(k\)-norm ⋮ Polyhedral separation via difference of convex (DC) programming

Uses Software

• LIBSVM

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