Support Vector Machine with feature selection: A multiobjective approach
Javier Alcaraz, Martine Labbé, Mercedes Landete
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 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.