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TKDD
2016

Adaptive Model Rules From High-Speed Data Streams

3 years 14 days ago
Adaptive Model Rules From High-Speed Data Streams
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.
João Duarte, João Gama, Albert Bifet
Added 11 Apr 2016
Updated 11 Apr 2016
Type Journal
Year 2016
Where TKDD
Authors João Duarte, João Gama, Albert Bifet
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