Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/83025
Author(s): | Duarte, J João Gama |
Title: | Ensembles of Adaptive Model Rules from High-Speed Data Streams |
Issue Date: | 2014 |
Abstract: | 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 |
Source: | 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 |
Document Type: | Artigo em Livro de Atas de Conferência Internacional |
Rights: | openAccess |
License: | https://creativecommons.org/licenses/by-nc/4.0/ |
Appears in Collections: | FEP - Artigo em Livro de Atas de Conferência Internacional |
Files in This Item:
File | Description | Size | Format | |
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115086.pdf | Ensembles of Adaptive Model Rules from High-Speed Data Streams | 760.61 kB | Adobe PDF | View/Open |
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