Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/83025
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dc.creatorDuarte, J
dc.creatorJoão Gama
dc.date.accessioned2019-01-31T09:44:49Z-
dc.date.available2019-01-31T09:44:49Z-
dc.date.issued2014
dc.identifier.othersigarra:115086
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/83025-
dc.description.abstractThe volume and velocity of data is increasing at astonishing rates. In order to extract knowledge from this huge amount of information there is a need for efficient on-line learning algorithms. Rule-based algorithms produce models that are easy to understand and can be used almost offhand. Ensemble methods combine several predicting models to improve the quality of prediction. In this paper, a new on-line ensemble method that combines a set of rule-based models is proposed to solve regression problems from data streams. Experimental results using synthetic and real time-evolving data streams show the proposed method significantly improves the performance of the single rule-based learner, and outperforms two state-of-the-art regression algorithms for data streams.
dc.language.isoeng
dc.relation.ispartofProceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine 2014, New York City, USA, August 24, 2014
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleEnsembles of Adaptive Model Rules from High-Speed Data Streams
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Economia
dc.identifier.authenticusP-00G-6BM
Appears in Collections:FEP - Artigo em Livro de Atas de Conferência Internacional

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