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Embedded variable selection method using signomial classification

Author

Listed:
  • Kyoungmi Hwang

    (Samsung Electronics)

  • Dohyun Kim

    (Myongji University)

  • Kyungsik Lee

    (Seoul National University)

  • Chungmok Lee

    (Hankuk University of Foreign Studies)

  • Sungsoo Park

    (KAIST)

Abstract

We propose two variable selection methods using signomial classification. We attempt to select, among a set of the input variables, the variables that lead to the best performance of the classifier. One method repeatedly removes variables based on backward selection, whereas the second method directly selects a set of variables by solving an optimization problem. The proposed methods conduct variable selection considering nonlinear interactions of variables and obtain a signomial classifier with the selected variables. Computational results show that the proposed methods more effectively selects desirable variables for predicting output and provide the classifiers with better or comparable test error rates, as compared with existing methods.

Suggested Citation

  • Kyoungmi Hwang & Dohyun Kim & Kyungsik Lee & Chungmok Lee & Sungsoo Park, 2017. "Embedded variable selection method using signomial classification," Annals of Operations Research, Springer, vol. 254(1), pages 89-109, July.
    • Handle: RePEc:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2445-z
    DOI: 10.1007/s10479-017-2445-z
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    References listed on IDEAS

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    1. E. L. Lawler & D. E. Wood, 1966. "Branch-and-Bound Methods: A Survey," Operations Research, INFORMS, vol. 14(4), pages 699-719, August.
    2. Kyungsik Lee & Norman Kim & Myong Jeong, 2014. "The sparse signomial classification and regression model," Annals of Operations Research, Springer, vol. 216(1), pages 257-286, May.
    3. P. S. Bradley & O. L. Mangasarian & W. N. Street, 1998. "Feature Selection via Mathematical Programming," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 209-217, May.
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    Cited by:

    1. Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.
    2. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    3. Young Woong Park & Diego Klabjan, 2020. "Subset selection for multiple linear regression via optimization," Journal of Global Optimization, Springer, vol. 77(3), pages 543-574, July.

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