Ensemble OS-ELM based on combination weight for data stream classification
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For online classification, how to design a self-adapted model is a challenging task. To make the model easily adaptable for the fast-changing data stream, a novel ensemble OS-ELM has been put forward. Different from traditional ensemble methods, the proposed approach provided a new self-adapted weight update algorithm. In online learning stage, both the current prediction accuracy and history record are considered. Based on suffer loss and the norm of output layer vector, an aggregate model of game theory is adopted to calculate the combination weight. This strategy fully considers the differences of individual learners. It helps the ensemble method reduce the fitting error of sequence fragment. Also, alterative hidden-layer output matrix can be calculated according to the current fragment, thus building the steady network architecture in the next chunk. So interactive parameter optimization is avoided and the automatic model is suitable for online learning. Numerical experiments are conducted on eight different kinds of UCI datasets. The results demonstrate that the proposed algorithm not only has better generalisation performance but also provides faster learning procedure.
KeywordsData stream classification Ensemble OS-ELM Game theory Online learning
The work was supported by the National Key Research Project of China under Grant No.2016YFB1001304, the National Natural Science Foundation of China under Grant 61572229, the JLUSTIRT High-level Innovation Team, and the Fundamental Research Funds for Central Universities under Grant No.2017TD-19. The authors gratefully acknowledge financial support from the Research Centre for Intelligent Signal Identification and Equipment, Jilin Province.
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