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Multi-class classification using a signomial function

Author

Listed:
  • Kyoungmi Hwang

    (KAIST, Daejeon, Korea (South), Republic of Korea)

  • Kyungsik Lee

    (Seoul National University, Seoul, Republic of Korea; Hankuk University of Foreign Studies, Yongin-si, Republic of Korea)

  • Chungmok Lee

    (IBM Research—Ireland, Dublin, Ireland)

  • Sungsoo Park

    (KAIST, Daejeon, Korea (South), Republic of Korea)

Abstract

We propose two multi-class classification methods using a signomial function. Each of these methods directly constructs a multi-class classifier by solving a single optimization problem. Since the number of possible signomial terms is extremely large, we propose a column generation method that iteratively generates good signomial terms. Both of these methods obtain better or comparable classification accuracies than existing methods and also provide more sparse classifiers.

Suggested Citation

  • Kyoungmi Hwang & Kyungsik Lee & Chungmok Lee & Sungsoo Park, 2015. "Multi-class classification using a signomial function," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 434-449, March.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:3:p:434-449
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    Cited by:

    1. Kyoungmi Hwang & Kyungsik Lee & Sungsoo Park, 2017. "Variable selection methods for multi-class classification using signomial function," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1117-1130, September.

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