期刊
NEUROCOMPUTING
卷 238, 期 -, 页码 433-449出版社
ELSEVIER
DOI: 10.1016/j.neucom.2016.12.078
关键词
Data stream; Classification; Concept drift; Extreme learning machine; Online learning
资金
- National Natural Science Foundation of China [U1435212, 61432011, 61202018, 61303008]
- National Key Basic Research and Development Program of China (973) [2013CB329404]
In our society, many fields have produced a large number of data streams. How to mining the interesting knowledge and patterns from continuous data stream becomes a problem which we have to solve. Different from conventional classification algorithms, data stream classification algorithms have to adjust their classification models with the change of data stream because of concept drift. However, conventional classification models will keep stable once models are trained. To solve the problem, a dynamic extreme learning machine for data stream classification (DELM) is proposed. DELM utilizes online learning mechanism to train ELM as basic classifier and trains a double hidden layer structure to improve the performance of ELM. When an alert about concept drift is set, more hidden layer nodes are added into ELM to improve the generalization ability of classifier. If the value measuring concept drift reaches the upper limit or the accuracy of ELM is in a low level, the current classifier will be deleted, and the algorithm will use new data to train a new classifier so as to learn new concept. The experimental results showed DELM could improve the accuracy of classification result, and can adapt to new concept in a short time. (C) 2017 Elsevier B.V. All rights reserved.
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