RT Journal Article
T1 CURIE: a cellular automaton for concept drift detection: 												a cellular automaton for concept drift detection
A1 Lobo, Jesus L.
A1 Del Ser, Javier
A1 Osaba, Eneko
A1 Bifet, Albert
A1 Herrera, Francisco
AB 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.
SN 1384-5810
YR 2021
FD 2021-11
LA eng
NO Lobo , 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
NO Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.
DS TECNALIA Publications
RD 29 sept 2024