Mixed Integer Linear Programming for Feature Selection in Support Vector Machine

Martine Labbé 1 Luisa I. Martínez-Merino 2 Antonio M. Rodríguez-Chía 2
1 INOCS - Integrated Optimization with Complex Structure
ULB - Université Libre de Bruxelles [Bruxelles], Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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 modelled 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. We propose 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.
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Martine Labbé, Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía. Mixed Integer Linear Programming for Feature Selection in Support Vector Machine. Discrete Applied Mathematics, Elsevier, 2019, 261, pp.276-304. ⟨10.1016/j.dam.2018.10.025⟩. ⟨hal-01924379⟩



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