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Kernel variable selection for multicategory support vector machines

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  • Park, Beomjin
  • Park, Changyi

Abstract

Variable selection is important in statistical learning because it can increase predictive performances and yield interpretable models. Since support vector machines construct a classification model through a mapping from an original space to a high-dimensional feature space, it is difficult to select informative variables and interpret the relation between covariates and class labels. In this paper, we suggest a variable selection method for support vector machines, focusing on the multicategory problem. We study asymptotic properties of the proposed method. Also we illustrate that our method can accurately select relevant variables and yield interpretable models on both simulated and real data sets.

Suggested Citation

  • Park, Beomjin & Park, Changyi, 2021. "Kernel variable selection for multicategory support vector machines," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:jmvana:v:186:y:2021:i:c:s0047259x21000786
    DOI: 10.1016/j.jmva.2021.104800
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    References listed on IDEAS

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    1. Chong Zhang & Yufeng Liu, 2014. "Multicategory angle-based large-margin classification," Biometrika, Biometrika Trust, vol. 101(3), pages 625-640.
    2. Fan, Junliang & Wu, Lifeng & Ma, Xin & Zhou, Hanmi & Zhang, Fucang, 2020. "Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions," Renewable Energy, Elsevier, vol. 145(C), pages 2034-2045.
    3. Yoonkyung Lee & Yuwon Kim & Sangjun Lee & Ja-Yong Koo, 2006. "Structured multicategory support vector machines with analysis of variance decomposition," Biometrika, Biometrika Trust, vol. 93(3), pages 555-571, September.
    4. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
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    Cited by:

    1. Park, Beomjin & Park, Changyi, 2023. "Multiclass Laplacian support vector machine with functional analysis of variance decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).

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