Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10216/83025
Autor(es): | Duarte, J João Gama |
Título: | Ensembles of Adaptive Model Rules from High-Speed Data Streams |
Data de publicação: | 2014 |
Resumo: | The 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. |
URI: | https://hdl.handle.net/10216/83025 |
Fonte: | Proceedings 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 |
Tipo de Documento: | Artigo em Livro de Atas de Conferência Internacional |
Condições de Acesso: | openAccess |
Licença: | https://creativecommons.org/licenses/by-nc/4.0/ |
Aparece nas coleções: | FEP - Artigo em Livro de Atas de Conferência Internacional |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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115086.pdf | Ensembles of Adaptive Model Rules from High-Speed Data Streams | 760.61 kB | Adobe PDF | Ver/Abrir |
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