Ensembles of Adaptive Model Rules from High-Speed Data Streams

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Duarte, J (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: 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

Pages: 198-213

Other information

Authenticus ID: P-00G-6BM

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

Notes: http://www.jmlr.org/proceedings/papers/v36/duarte14.html