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An Ensemble Extreme Learning Machine for Data Stream Classification

by 1, 1 and 2,*
1
School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2
School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Algorithms 2018, 11(7), 107; https://doi.org/10.3390/a11070107
Received: 14 June 2018 / Revised: 29 June 2018 / Accepted: 11 July 2018 / Published: 17 July 2018
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time. View Full-Text
Keywords: extreme learning machine; data stream classification; online learning; concept drift detection extreme learning machine; data stream classification; online learning; concept drift detection
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Yang, R.; Xu, S.; Feng, L. An Ensemble Extreme Learning Machine for Data Stream Classification. Algorithms 2018, 11, 107.

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