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dc.contributor.authorHmida, Hmida
dc.contributor.authorBen Hamida, Sana
dc.contributor.authorBorgi, Amel
dc.contributor.authorRukoz, Marta
dc.date.accessioned2019-03-18T14:40:34Z
dc.date.available2019-03-18T14:40:34Z
dc.date.issued2018
dc.identifier.issn1877-0509
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18537
dc.language.isoenen
dc.subjectCartesian Genetic Programmingen
dc.subjectActive Samplingen
dc.subjectHiggs Bosons Classificationen
dc.subjectlarge dataseten
dc.subjectMachine Learningen
dc.subject.ddc006.3en
dc.titleScale Genetic Programming for large Data Sets: Case of Higgs Bosons Classificationen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenExtract knowledge and significant information from very large data sets is a main topic in Data Science, bringing the interest of researchers in machine learning field. Several machine learning techniques have proven effective to deal with massive data like Deep Neuronal Networks. Evolutionary algorithms are considered not well suitable for such problems because of their relatively high computational cost. This work is an attempt to prove that, with some extensions, evolutionary algorithms could be an interesting solution to learn from very large data sets. We propose the use of the Cartesian Genetic Programming (CGP) as meta-heuristic approach to learn from the Higgs big data set. CGP is extended with an active sampling technique in order to help the algorithm to deal with the mass of the provided data. The proposed method is able to take up the challenge of dealing with the complete benchmark data set of 11 million events and produces satisfactory preliminary results.en
dc.relation.isversionofjnlnameProcedia Computer Science
dc.relation.isversionofjnlvol126en
dc.relation.isversionofjnldate2018-08
dc.relation.isversionofjnlpages302-311en
dc.relation.isversionofdoi10.1016/j.procs.2018.07.264en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelIntelligence artificielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-03-18T14:35:54Z
hal.person.labIds989
hal.person.labIds989
hal.person.labIds253759
hal.person.labIds989
hal.identifierhal-02071412*


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