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Support Vector Machine with feature selection: A multiobjective approach

Javier Alcaraz 1 Martine Labbé 2 Mercedes Landete 1 
2 INOCS - Integrated Optimization with Complex Structure
Inria Lille - Nord Europe, ULB - Université libre de Bruxelles, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Support Vector Machines are models widely used in supervised classification. The classical model minimizes a compromise between the structural risk and the empirical risk. In this paper, we consider the Support Vector Machine with feature selection and we design and implement a bi-objective evolutionary algorithm for approximating the Pareto optimal frontier of the two objectives. The metaheuristic is based on the nondominated sorting genetic algorithm and includes problem-specific knowledge. To demonstrate the efficiency of the algorithm proposed, we have carried out extensive computational experiments comparing the Paretofrontiers given by the exact method AUGMECON2 and the metaheuristic approach respectively in a set of well known instances. In this paper, we also discuss some properties of the points in the Pareto frontier.
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https://hal-cnrs.archives-ouvertes.fr/hal-03824598
Contributor : Martine Labbé Connect in order to contact the contributor
Submitted on : Friday, October 21, 2022 - 3:48:57 PM
Last modification on : Tuesday, November 22, 2022 - 2:26:16 PM

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Javier Alcaraz, Martine Labbé, Mercedes Landete. Support Vector Machine with feature selection: A multiobjective approach. Expert Systems with Applications, 2022, 204, pp.117485. ⟨10.1016/j.eswa.2022.117485⟩. ⟨hal-03824598⟩

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