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Fuzzy large margin distribution machine for classification

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Abstract

As a variant of Support Vector Machine (SVM), Large Margin Distribution Machine (LDM) has been validated to outperform SVM both theoretically and experimentally. Due to the inevitable noise in real applications, the credibility of different samples is not necessarily the same, which is neglected by most existing LDM models. To tackle the above problem, this paper first introduces fuzzy set theory into LDM, and proposes a Fuzzy Large Margin Distribution Machine (FLDM) with better robustness and performance. Considering the noise and uncertainty in datasets, sample points farther from the center of homogenous class are less reliable. Therefore, a fuzzy membership function based on the distance to the class center is utilized to characterize the confidence of each sample, i.e., the degree to which the sample belongs to a certain category. Furthermore, different strategies are developed to obtain class centers for linearly separable and linearly inseparable problems. Experiments conducted on both artificial and UCI datasets verified the superiority of FLDM from different perspectives.

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Data Availibility Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (Nos. 62106205 and 62276217), the Natural Science Foundation of Chongqing (Nos. cstc2021jcyj-msxmX0824 and cstc2021jcyj-msxmX0565), the Project of Science and Technology Research Program of Chongqing Education Commission of China (Nos. KJQN202100207 and KJZDK202100203).

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Authors and Affiliations

  1. School of Artificial Intelligence, Southwest University, Chongqing, 400715, People’s Republic of China

    Denghao Dong, Minyu Feng & Libo Zhang

  2. Potsdam Institute for Climate Impact Research, 14473, Potsdam, Germany

    Jürgen Kurths

  3. Department of Physics, Humboldt University, 12489, Berlin, Germany

    Jürgen Kurths

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  1. Denghao Dong
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  2. Minyu Feng
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  3. Jürgen Kurths
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  4. Libo Zhang
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Correspondence to Libo Zhang.

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Dong, D., Feng, M., Kurths, J. et al. Fuzzy large margin distribution machine for classification. Int. J. Mach. Learn. & Cyber. 15, 1891–1905 (2024). https://doi.org/10.1007/s13042-023-02004-3

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  • DOI: https://doi.org/10.1007/s13042-023-02004-3

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