Abstract
Extreme learning machine (ELM) techniques have received considerable attention in computational intelligence and machine learning communities, because of the significantly low computational time. ELM provides solutions to regression, clustering, binary classification, multiclass classifications and so on, but not to multi-label learning. A thresholding method based ELM is proposed in this paper to adapted ELM for multi-label classification, called extreme learning machine for multi-label classification (ELM-ML). In comparison with other multi-label classification methods, ELM-ML outperforms them in several standard data sets in most cases, especially for applications which only have small labeled data set.
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Acknowledgements
The authors wish to thank the anonymous reviewers for their helpful comments and suggestions. The author also thanks Prof. Zhihua Zhou, Mingling Zhang and Jianhua Xu, whose software and data have been used in our experiments. The authors also thank Changmeng Jiang and Jingting Xu for doing some related experiments. This work was supported by NSFc 61202184 and Scientific research plan projects 2015JQ6240 and 2013JK1152.
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Sun, X. et al. (2016). ELM-ML: Study on Multi-label Classification Using Extreme Learning Machine. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_9
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DOI: https://doi.org/10.1007/978-3-319-28373-9_9
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