Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44320
DC FieldValueLanguage
dc.contributor.authorCosta, Joana-
dc.contributor.authorSilva, Catarina-
dc.contributor.authorAntunes, Mário-
dc.contributor.authorRibeiro, Bernardete-
dc.date.accessioned2017-11-08T20:53:43Z-
dc.date.available2017-11-08T20:53:43Z-
dc.date.issued2017-
dc.identifier.urihttp://hdl.handle.net/10316/44320-
dc.description.abstractNowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn++.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn++.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).por
dc.language.isoengpor
dc.rightsopenAccesspor
dc.subjectDynamic environmentspor
dc.subjectEnsemblesLearn++.NSETwitterpor
dc.titleAdaptive learning for dynamic environments: A comparative approachpor
dc.typearticle-
degois.publication.firstPage336por
degois.publication.lastPage345por
degois.publication.titleEngineering Applications of Artificial Intelligencepor
dc.peerreviewedyespor
dc.identifier.doi10.1016/j.engappai.2017.08.004-
degois.publication.volume65por
uc.controloAutoridadeSim-
item.fulltextCom Texto completo-
item.languageiso639-1en-
item.grantfulltextopen-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCenter for Research in Neuropsychology and Cognitive Behavioral Intervention-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0002-0942-936X-
crisitem.author.orcid0000-0002-9770-7672-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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