Mostrar registro simples

dc.contributor.advisorValiati, João Francisco
dc.contributor.authorNunes, André Luís
dc.date.accessioned2017-06-13T14:22:04Z
dc.date.accessioned2022-09-22T19:25:31Z
dc.date.available2017-06-13T14:22:04Z
dc.date.available2022-09-22T19:25:31Z
dc.date.issued2017-03-28
dc.identifier.urihttps://hdl.handle.net/20.500.12032/60718
dc.description.abstractThe explosion of data volume and its expansion speed make tasks of finding knowledge and analyzing data challenging, even more so when non-stationary bases are considered. Although the future values prediction plays a fundamental role in areas such as climate, routing problems and economics, among others, classification seems to be still the most exploited task. Recently, some value-regression algorithms have been launched, for example: FIMT-DD, AMRules, IBLStreams and SFNRegressor; however, their investigative studies have explored more aspects of innovation and analysis of error prediction than exploring their capabilities through criteria that are considered fundamental to data stream, such as elapsed time and memory. In this way, the objective of this work is to present an investigative study about these algorithms that treat regression considering dynamic environments, using massive databases, and also explore the algorithm's adaptability capacity with the presence of concept drift. In order to do this, three databases were analyzed and extended to explore the main evaluation criteria adopted. A wide experiment was carried out, which produced a comparison of the results obtained with the chosen algorithms, allowing to generate behavior indication of each one through the different scenarios to which were exposed. Thus, the main contributions of this work are: evaluation of fundamental criteria: memory, execution time and power of generalization, related to regression to data stream; production of a critical analysis of the algorithms investigated; and the possibility of reproducing and extending the studies carried out by making available the parametrizations applyed.en
dc.description.sponsorshipNenhumapt_BR
dc.languagept_BRpt_BR
dc.publisherUniversidade do Vale do Rio dos Sinospt_BR
dc.rightsopenAccesspt_BR
dc.subjectMineração de data streampt_BR
dc.subjectConcept driften
dc.titleUm estudo investigativo de algoritmos de regressão para data streamspt_BR
dc.typeDissertaçãopt_BR


Arquivos deste item

ArquivosTamanhoFormatoVisualização
André Luís Nunes_.pdf2.523Mbapplication/pdfVisualizar/Abrir

Este item aparece na(s) seguinte(s) coleção(s)

Mostrar registro simples


© AUSJAL 2022

Asociación de Universidades Confiadas a la Compañía de Jesús en América Latina, AUSJAL
Av. Santa Teresa de Jesús Edif. Cerpe, Piso 2, Oficina AUSJAL Urb.
La Castellana, Chacao (1060) Caracas - Venezuela
Tel/Fax (+58-212)-266-13-41 /(+58-212)-266-85-62

Nuestras redes sociales

facebook Facebook

twitter Twitter

youtube Youtube

Asociaciones Jesuitas en el mundo
Ausjal en el mundo AJCU AUSJAL JESAM JCEP JCS JCAP