Abstract
In this paper, instead of modifying the framework of Extreme learning machine (ELM), we propose a learning algorithm to improve generalization ability of ELM with Synthetic Instances Generation (SIGELM). We focus on optimizing the output-layer weights via adding informative synthetic instances to the training dataset at each learning step. In order to get the required synthetic instances, a neighborhood is determined for each high-uncertainty training sample and then the synthetic instances which enhance the training performance of ELM are selected in the neighborhood. The experimental results based on 4 representative regression datasets of KEEL demonstrate that our proposed SIGELM obviously improves the generalization capability of ELM and effectively decreases the phenomenon of over-fitting.
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Acknowledgments
The first author and corresponding author contributed equally the same to this article which is supported by National Natural Science Foundations of China (61503252 and 61473194) and China Postdoctoral Science Foundation (2016T90799).
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Ao, W., He, Y., Huang, J.Z., He, Y. (2017). Improving Generalization Capability of Extreme Learning Machine with Synthetic Instances Generation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_1
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