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dc.contributor.authorMendoza, Jessica
dc.contributor.authorPalacios, David
dc.contributor.authorDe la Bandera Cascales, Isabel
dc.contributor.authorBarco-Moreno, Raquel 
dc.date.accessioned2019-10-30T08:27:45Z
dc.date.available2019-10-30T08:27:45Z
dc.date.created2019
dc.date.issued2019-10-30
dc.identifier.urihttps://hdl.handle.net/10630/18667
dc.description.abstractThe growth in the number of services, functionalities and users expected with the arrival of the 5th Generation (5G) of mobile communications networks will produce an increase in the complexity of the network management tasks. These tasks should be adapted to meet the requirements of zero latency and great bandwidth presented by 5G new services. Thus, traditional management reactive approach should evolve to a proactive one. In this context, prediction techniques will become an essential tool. At the same time, 5G management algorithm will use both internal (network information) and external (context information) information sources. Thus, 5G management tasks should handle large amounts of data. The use of large dataset to generate prediction models can lead to the lack of precision of these models, due to the sparsity suffered by the data. In this paper, the use of feature selection techniques to improve prediction models is proposed.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isospaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRedes de informaciónen_US
dc.subjectSistemas de comunicaciones inhalámbricosen_US
dc.subjectTelecomunicacionesen_US
dc.titleSelección automática de características para una gestión proactiva eficiente de redes 5Gen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.relation.eventtitleXXXIV Simposio Nacional de la Unión Científica Internacional de Radio, URSI 2019en_US
dc.relation.eventplaceSevillaen_US
dc.relation.eventdate4 septiembre 2019en_US


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