CURIE: a cellular automaton for concept drift detection: a cellular automaton for concept drift detection

Simple item page
dc.contributor.authorLobo, Jesus L.
dc.contributor.authorDel Ser, Javier
dc.contributor.authorOsaba, Eneko
dc.contributor.authorBifet, Albert
dc.contributor.authorHerrera, Francisco
dc.contributor.institutionIA
dc.contributor.institutionQuantum
dc.date.issued2021-11
dc.descriptionPublisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.
dc.description.abstractData stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CURIECURIE, a drift detector relying on cellular automata. Specifically, in CURIECURIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CURIECURIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CURIECURIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.en
dc.description.statusPeer reviewed
dc.format.extent24
dc.format.extent966814
dc.identifier.citationLobo , J L , Del Ser , J , Osaba , E , Bifet , A & Herrera , F 2021 , ' CURIE: a cellular automaton for concept drift detection : a cellular automaton for concept drift detection ' , Data Mining and Knowledge Discovery , vol. 35 , no. 6 , pp. 2655-2678 . https://doi.org/10.1007/s10618-021-00776-2
dc.identifier.doi10.1007/s10618-021-00776-2
dc.identifier.issn1384-5810
dc.identifier.otherresearchoutputwizard: 11556/1188
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85114174644&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofData Mining and Knowledge Discovery
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsConcept drift
dc.subject.keywordsDrift detection
dc.subject.keywordsData stream mining
dc.subject.keywordsCellular automata
dc.subject.keywordsConcept drift
dc.subject.keywordsDrift detection
dc.subject.keywordsData stream mining
dc.subject.keywordsCellular automata
dc.subject.keywordsInformation Systems
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsComputer Networks and Communications
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/783163/EU/Integrated Development 4.0/iDev40
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/783163/EU/Integrated Development 4.0/iDev40
dc.subject.keywordsFunding Info
dc.subject.keywordsThis work has received funding support from the ECSEL Joint Undertaking (JU) under grant agreement No 783163 (iDev40 project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme, national grants from Austria, Belgium, Germany, Italy, Spain and Romania, as well as the European Structural and Investment Funds. Authors would like to also thank the ELKARTEK and EMAITEK funding programmes of the Basque Government (Spain)
dc.subject.keywordsThis work has received funding support from the ECSEL Joint Undertaking (JU) under grant agreement No 783163 (iDev40 project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme, national grants from Austria, Belgium, Germany, Italy, Spain and Romania, as well as the European Structural and Investment Funds. Authors would like to also thank the ELKARTEK and EMAITEK funding programmes of the Basque Government (Spain)
dc.titleCURIE: a cellular automaton for concept drift detection: a cellular automaton for concept drift detectionen
dc.typejournal article
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Lobo2021_Article_CURIEACellularAutomatonForConc.pdf
Size:
944.15 KB
Format:
Adobe Portable Document Format
Description:
Download