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Kernel risk-sensitive mean p-power error based robust extreme learning machine for classification

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Abstract

Recently, extreme learning machine (ELM) has attracted a lot of attention due to its high performance and extreme speed. However, how to improve the robustness of ELM has always been a problem. Considering the problems of noise and outliers in the experimental data, in this paper, we introduce the kernel risk-sensitive mean p-power error (KRP) into ELM and propose a robust ELM method named kernel risk-sensitive mean p-power error based robust extreme learning machine (KRPELM), on the basis of the high efficiency and robustness of KRP. In KRPELM, KRP function instead of square loss is integrated into ELM as the loss function, which can improve the robustness of ELM to noise and outliers. We also propose an efficient iterative adjustment strategy to optimize KRPELM. Nine benchmark datasets are utilized to verify the classification performance of the proposed KRPELM. In addition, we apply the proposed method to the classification of cancer samples. The experimental results on five cancer gene expression datasets show that KRPELM can identify different cancer types more accurately.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61872220, and 61972226.

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Authors and Affiliations

  1. School of Computer Science, Qufu Normal University, Rizhao, 276826, Shandong, China

    Liang-Rui Ren, Junliang Shang & Jin-Xing Liu

  2. Qufu Normal University Library, Qufu Normal University, Rizhao, 276826, Shandong, China

    Ying-Lian Gao

Authors
  1. Liang-Rui Ren
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  2. Ying-Lian Gao
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  3. Junliang Shang
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  4. Jin-Xing Liu
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Contributions

LRR and JXL contributed to the design of the study. LRR proposed the KRPELM method, performed the experiments, and drafted the manuscript. YLG and JLS contributed to the data analysis. YLG contributed to improving the writing of manuscripts. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jin-Xing Liu.

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The authors declare that they have no conflict of interest.

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Cite this article

Ren, LR., Gao, YL., Shang, J. et al. Kernel risk-sensitive mean p-power error based robust extreme learning machine for classification. Int. J. Mach. Learn. & Cyber. 13, 199–216 (2022). https://doi.org/10.1007/s13042-021-01391-9

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  • DOI: https://doi.org/10.1007/s13042-021-01391-9

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