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Support Vector Machine with feature selection: A multiobjective approach

par Alcaraz, Javier;Labbé, Martine ;Landete, Mercedes
Référence Expert systems with applications, 204, 117485
Publication Publié, 2022-10

Article révisé par les pairs

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Résumé :

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 non-dominated 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 Pareto-frontiers 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.