Publication:
A survey on machine learning for recurring concept drifting data streams

Simple item page
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorSuárez Cetrulo, Andrés L.
dc.contributor.authorQuintana, David
dc.contributor.authorCervantes, Alejandro
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2023-09-29T09:50:18Z
dc.date.available2023-09-29T09:50:18Z
dc.date.issued2023-03-01
dc.description.abstractThe problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time.en
dc.description.sponsorshipWe would like to thank the editor and the anonymous reviewers for their thoughtful and detailed comments on our paper. We would also like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin).en
dc.format.extent17
dc.identifier.bibliographicCitationSuárez‐Cetrulo, A. L., Quintana, D., & Cervantes, A. (2023). A survey on Machine Learning for recurring concept drifting data streams. Expert Systems with Applications, 213, 118934.en
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2022.118934
dc.identifier.issn0957-4174
dc.identifier.publicationfirstpage1
dc.identifier.publicationissuePart A, 118934en
dc.identifier.publicationlastpage17
dc.identifier.publicationtitleExpert Systems with Applicationsen
dc.identifier.publicationvolume213
dc.identifier.urihttps://hdl.handle.net/10016/38481
dc.identifier.uxxiAR/0000033398
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. PGC2018-096849-B-I00es
dc.relation.projectIDAT-2022es
dc.rights© 2022 The Authors.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaEconomíaes
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherRegime changeen
dc.subject.otherOnline machine learningen
dc.subject.otherData streamsen
dc.subject.otherConcept driften
dc.subject.otherMeta learningen
dc.titleA survey on machine learning for recurring concept drifting data streamsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
survey_ESWA_2023.pdf
Size:
1.12 MB
Format:
Adobe Portable Document Format
Download
Collections
DI - Artículos de Revistas