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New ensemble methods for evolving data streams
dc.contributor.author | Bifet Figuerol, Albert Carles |
dc.contributor.author | Holmes, Geoffrey |
dc.contributor.author | Pfahringer, Bernhard |
dc.contributor.author | Kirkby, R |
dc.contributor.author | Gavaldà Mestre, Ricard |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2018-11-28T18:03:01Z |
dc.date.issued | 2009 |
dc.identifier.citation | Bifet, A.C., Holmes, G., Pfahringer, B., Kirkby, R., Gavaldà, R. New ensemble methods for evolving data streams. A: ACM SIGKDD Conference on Knowledge Discovery and Data Mining. "KDD'09: proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: June 28-July 1, 2009: Paris, France". New York: Association for Computing Machinery (ACM), 2009, p. 139-148. |
dc.identifier.isbn | 978-1-60558-495-9 |
dc.identifier.uri | http://hdl.handle.net/2117/125201 |
dc.description.abstract | Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such data streams evolve over time, that is when concepts drift or change completely, is becoming one of the core issues. When tackling non-stationary concepts, ensembles of classifiers have several advantages over single classifier methods: they are easy to scale and parallelize, they can adapt to change quickly by pruning under-performing parts of the ensemble, and they therefore usually also generate more accurate concept descriptions. This paper proposes a new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. Using the new experimental framework, an evaluation study on synthetic and real-world datasets comprising up to ten million examples shows that the new ensemble methods perform very well compared to several known methods. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Association for Computing Machinery (ACM) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Bases de dades |
dc.subject.lcsh | Data mining |
dc.subject.other | Data streams |
dc.subject.other | Ensemble methods |
dc.subject.other | Concept drift |
dc.subject.other | Decision trees |
dc.title | New ensemble methods for evolving data streams |
dc.type | Conference report |
dc.subject.lemac | Mineria de dades |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.identifier.doi | 10.1145/1557019.1557041 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://dl.acm.org/citation.cfm?doid=1557019.1557041 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 2339605 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MEC/5PN/TIN2005-08832-C03-03 |
dc.date.lift | 10000-01-01 |
local.citation.author | Bifet, A.C.; Holmes, G.; Pfahringer, B.; Kirkby, R.; Gavaldà, R. |
local.citation.contributor | ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
local.citation.pubplace | New York |
local.citation.publicationName | KDD'09: proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: June 28-July 1, 2009: Paris, France |
local.citation.startingPage | 139 |
local.citation.endingPage | 148 |