Support Vector Machine with feature selection: A multiobjective approach

Alcaraz, Javier; Labbé, Martine; Landete, Mercedes

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1016/j.eswa.2022.117485

Title :

Support Vector Machine with feature selection: A multiobjective approach

Author(s) :

Alcaraz, Javier [Auteur]
Universidad Miguel Hernández [Elche] [UMH]
Labbé, Martine [Auteur]
Integrated Optimization with Complex Structure [INOCS]
Landete, Mercedes [Auteur]
Universidad Miguel Hernández [Elche] [UMH]

Journal title :

Expert systems with applications

Pages :

117485

Publisher :

Elsevier

Publication date :

2022-05-11

ISSN :

0957-4174

English keyword(s) :

Support vector machine
Feature selection
Multi-objective optimization
NSGA-II
AUGMECON2

HAL domain(s) :

Computer Science [cs]/Operations Research [math.OC]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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