期刊
NEUROCOMPUTING
卷 275, 期 -, 页码 2864-2879出版社
ELSEVIER
DOI: 10.1016/j.neucom.2017.11.030
关键词
Online learning; Extreme learning machine; Memory factor; Similarity
资金
- National Natural Science Foundation of China [61702070, 61425002, 61672121, 61672051, 61572093, 61402066, 61402067, 61370005, 31370778]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT_15R07]
- Program for Liaoning Innovative Research Team in University [LT2015002]
- Basic Research Program of the Key Lab in Liaoning Province Educational Department [LZ2014049, LZ2015004]
- Scientific Research Fund of Liaoning Provincial Education [L2015015, L2014499]
- Program for Liaoning Key Lab of Intelligent Information Processing and Network Technology in University
In online learning, the contribution of old samples to a model decreases as time passes, and old samples gradually become invalid. Although the Online Sequential Extreme Learning Machine (OS-ELM) can avoid the repetitive training of old samples, invalid samples are still used, which goes against improving the accuracy of an OS-ELM model. The Online Sequence Extreme Learning Machine with Forgetting Mechanism (FOS-ELM) timely discards invalid samples, but it does not consider the differences among valid samples and then has the limitation on boosting the accuracy and generalization. To solve this issue, the Memory Degradation Based OS-ELM (MDOS-ELM) is proposed in this paper. The MDOS-ELM adjusts the weights of the old and new samples in real time by a self-adaptive memory factor, and simultaneously discards invalid samples. The self-adaptive memory factor is determined by two elements. One is the similarity between the new and old samples, and the other is the prediction errors of the current training samples on the previous model. The performance of the proposed MDOS-ELM is validated on both regression and classification datasets which include an artificial dataset and twenty-two real-world dataset. The results demonstrate that the MDOS-ELM model outperforms the OS-ELM and the FOS-ELM models on the accuracy and generalization. (C) 2017 Elsevier B.V. All rights reserved.
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