Journal
ALGORITHMS
Volume 11, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/a11070107
Keywords
extreme learning machine; data stream classification; online learning; concept drift detection
Funding
- National Natural Science Fund of China [61672130, 61602082, 61370200]
- State Key Laboratory of Software Architecture [SKLSAOP1701]
- China Postdoctoral Science Foundation [2015M581331]
- Foundation of LiaoNing Educational Committee [201602151]
- MOE Research Center for Online Education of China [2016YB121]
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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.
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