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Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Study

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorHmida, Hmida
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorBen Hamida, Sana
HAL ID: 177299
ORCID: 0000-0003-4202-613X
hal.structure.identifierLaboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
dc.contributor.authorBorgi, Amel
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorRukoz, Marta
dc.date.accessioned2019-03-18T14:04:13Z
dc.date.available2019-03-18T14:04:13Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18535
dc.language.isoenen
dc.subjectSamplingen
dc.subjectmachine learningen
dc.subjectdecision support systemsen
dc.subjectBig dataen
dc.subject.ddc006.3en
dc.titleSampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Studyen
dc.typeCommunication / Conférence
dc.description.abstractenThe amount of available data for data mining, knowledge discovery continues to grow very fast with the era of Big Data. Genetic Programming algorithms (GP), that are efficient machine learning techniques, are face up to a new challenge that is to deal with the mass of the provided data. Active Sampling, already used for Active Learning, might be a good solution to improve the Evolutionary Algorithms (EA) training from very big data sets. This paper investigates the adaptation of Topology Based Selection (TBS) to face massive learning datasets by means of Hierarchical Sampling. We propose to combine the Random Subset Selection (RSS) with the TBS to create the RSS-TBS method. Two variants are implemented, applied to solve the KDD intrusion detection problem. They are compared to the original RSS, TBS techniques. The experimental results show that the important computational cost generated by original TBS when applied to large datasets can be lightened with the Hierarchical Sampling.en
dc.identifier.citationpages50-60en
dc.relation.ispartoftitleAdvances in Big Data : Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greeceen
dc.relation.ispartofeditorAngelov, Plamen
dc.relation.ispartofeditorManolopoulos, Yannis
dc.relation.ispartofeditorIliadis, Lazaros
dc.relation.ispartofeditorRoy, Asim
dc.relation.ispartofeditorVellasco, Marley
dc.relation.ispartofpublnameSpringer International Publishingen
dc.relation.ispartofpublcityChamen
dc.relation.ispartofdate2017
dc.relation.ispartofpages348en
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-319-47897-5en
dc.relation.conftitle2nd INNS Conference on Big Dataen
dc.relation.confdate2016-10
dc.relation.confcityThessalonikien
dc.relation.confcountryGreeceen
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-319-47898-2_6en
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-03-18T13:43:04Z
hal.faultCode{"duplicate-entry":{"hal-01429907":{"doi":"1.0"}}}
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