Lobo, Jesus L.Del Ser, JavierOsaba, EnekoBifet, AlbertHerrera, Francisco2021-11Lobo , 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/s10618-021-00776-21384-5810researchoutputwizard: 11556/1188Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.Data 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.24966814enginfo:eu-repo/semantics/openAccessjournal article10.1007/s10618-021-00776-2Concept driftDrift detectionData stream miningCellular automataConcept driftDrift detectionData stream miningCellular automataInformation SystemsComputer Science ApplicationsComputer Networks and CommunicationsProject IDinfo:eu-repo/grantAgreement/EC/H2020/783163/EU/Integrated Development 4.0/iDev40info:eu-repo/grantAgreement/EC/H2020/783163/EU/Integrated Development 4.0/iDev40Funding InfoThis 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)This 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)http://www.scopus.com/inward/record.url?scp=85114174644&partnerID=8YFLogxK