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A wavelet extreme learning machine

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Abstract

Extreme learning machine (ELM) has been widely used in various fields to overcome the problem of low training speed of the conventional neural network. Kernel extreme learning machine (KELM) introduces the kernel method to ELM model, which is applicable in Stat ML. However, if the number of samples in Stat ML is too small, perhaps the unbalanced samples cannot reflect the statistical characteristics of the input data, so that the learning ability of Stat ML will be influenced. At the same time, the mix kernel functions used in KELM are conventional functions. Therefore, the selection of kernel function can still be optimized. Based on the problems above, we introduce the weighted method to KELM to deal with the unbalanced samples. Wavelet kernel functions have been widely used in support vector machine and obtain a good classification performance. Therefore, to realize a combination of wavelet analysis and KELM, we introduce wavelet kernel functions to KELM model, which has a mix kernel function of wavelet kernel and sigmoid kernel, and introduce the weighted method to KELM model to balance the sample distribution, and then we propose the weighted wavelet–mix kernel extreme learning machine. The experimental results show that this method can effectively improve the classification ability with better generalization. At the same time, the wavelet kernel functions perform very well compared with the conventional kernel functions in KELM model.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61379101), the National Key Basic Research Program of China (No. 2013CB329502), and the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China (No. BK20130209).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Jian Zhang, Xinzheng Xu & Yanan Zhang

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding

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  1. Shifei Ding
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  2. Jian Zhang
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  3. Xinzheng Xu
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  4. Yanan Zhang
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Correspondence to Shifei Ding.

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Cite this article

Ding, S., Zhang, J., Xu, X. et al. A wavelet extreme learning machine. Neural Comput & Applic 27, 1033–1040 (2016). https://doi.org/10.1007/s00521-015-1918-8

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  • DOI: https://doi.org/10.1007/s00521-015-1918-8

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