{"id":26063,"date":"2022-06-28T11:49:15","date_gmt":"2022-06-28T09:49:15","guid":{"rendered":"https:\/\/cio.umh.es\/?p=26063"},"modified":"2022-06-28T11:49:15","modified_gmt":"2022-06-28T09:49:15","slug":"alcaraz-j-labbe-m-landete-m-2022-support-vector-machine-with-feature-selection-a-multiobjective-approach-expert-systems-with-applications-204117485-2","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2022\/06\/28\/alcaraz-j-labbe-m-landete-m-2022-support-vector-machine-with-feature-selection-a-multiobjective-approach-expert-systems-with-applications-204117485-2\/","title":{"rendered":"Alcaraz, J., Labb\u00e9, M., Landete, M. (2022) \u201cSupport Vector Machine with feature selection: A multiobjective approach\u201d, Expert Systems with Applications, 204:117485"},"content":{"rendered":"<p class=\"AuthorHeader-module__syvlN margin-size-4-t\" style=\"text-align: justify\"><strong>Javier Alcaraz, Mercedes Landete (Operations Research Center, University Miguel Hern\u00e1ndez of Elche) and Martine Labb\u00e9 (Computer Science Department, Universit\u00e9 Libre de Bruxelles, Belgium)<\/strong><\/p>\n<p class=\"AuthorHeader-module__syvlN margin-size-4-t\" style=\"text-align: justify\"><strong>Abstract: <\/strong>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.<\/p>","protected":false},"excerpt":{"rendered":"<p>Javier Alcaraz, Mercedes Landete (Operations Research Center, University Miguel Hern\u00e1ndez of Elche) and Martine Labb\u00e9 (Computer Science Department, Universit\u00e9 Libre de Bruxelles, Belgium)<br \/>\nAbstract: 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 [&#8230;]<\/p>","protected":false},"author":5675,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[369888],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/26063"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/5675"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=26063"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/26063\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=26063"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=26063"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=26063"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}