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dc.contributor.authorRossi, André Luis Debiaso [UNESP]
dc.contributor.authorSoares, Carlos
dc.contributor.authorSouza, Bruno Feres de
dc.contributor.authorPonce de Leon Ferreira de Carvalho, André Carlos
dc.date.accessioned2021-06-25T10:55:55Z
dc.date.available2021-06-25T10:55:55Z
dc.date.issued2021-07-01
dc.identifierhttp://dx.doi.org/10.1016/j.ins.2021.02.075
dc.identifier.citationInformation Sciences, v. 565, p. 262-277.
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/11449/207483
dc.description.abstractData stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level.en
dc.format.extent262-277
dc.language.isoeng
dc.relation.ispartofInformation Sciences
dc.sourceScopus
dc.subjectAlgorithm selection
dc.subjectMeta-learning
dc.subjectTime-changing data
dc.titleMicro-MetaStream: Algorithm selection for time-changing dataen
dc.typeArtigo
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade do Porto
dc.contributor.institutionUniversidade Federal do Maranhão (UFMA)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.description.affiliationSão Paulo State University (Unesp), Campus of Itapeva
dc.description.affiliationFraunhofer Portugal AICOS and LIAAD-INESC TEC Faculdade de Engenharia Universidade do Porto
dc.description.affiliationUniversidade Federal do Maranhão (UFMA)
dc.description.affiliationInstituto de Ciências Matemáticas e de Computação Universidade de São Paulo
dc.description.affiliationUnespSão Paulo State University (Unesp), Campus of Itapeva
dc.identifier.doi10.1016/j.ins.2021.02.075
dc.identifier.scopus2-s2.0-85102862542
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