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Fuzzy ELM for classification based on feature space

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Abstract

As a competitive machine learning algorithm, extreme learning machine (ELM), with its simple theory and easy implementation, has been widely used in the field of pattern accuracy. Recently, researchers have proposed related research algorithms to accommodate noise and outlier data. With a proper fuzzy membership function, a fuzzy ELM can effectively reduce the effects of outliers when solving the classification problem. However, how to apply ELM for learning and accuracy in the presence of noise is still an important research topic. A novel fuzzy ELM (ANFELM) technique is proposed in this paper. In the algorithm, the membership degree of the sample is calculated in a feature mapping space instead of the data input space. The algorithm provides good performance in reducing the effects of outliers and significantly improves classification accuracy and generalization. Experiments on UCI datasets and textual datasets show that the proposed algorithm significantly improves the classification capability of ELM and is superior to other algorithms.

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  1. http://www.cad.zju.edu.cn/home/dengcai/Data/TextData.html

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Acknowledgements

This work is partially supported by grant from the Natural Science Foundation of China (No. 61632011, 61572102, 61702080, 61602079) and the Fundamental Research Funds for the Central Universities (NO. DUT18ZD102, DUT17RC(3)016).

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

    Yonghe Chu, Hongfei Lin, Liang Yang, Dongyu Zhang, Shaowu Zhang & Yufeng Diao

  2. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116081, China

    Deqin Yan

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  1. Yonghe Chu
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  2. Hongfei Lin
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  3. Liang Yang
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  4. Dongyu Zhang
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  5. Shaowu Zhang
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  7. Deqin Yan
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Correspondence to Hongfei Lin.

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Chu, Y., Lin, H., Yang, L. et al. Fuzzy ELM for classification based on feature space. Multimed Tools Appl 79, 27439–27464 (2020). https://doi.org/10.1007/s11042-019-08321-6

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  • DOI: https://doi.org/10.1007/s11042-019-08321-6

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