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Identification of best discrimination surface by mixed-integer semi-definite programming for support vector machine

Author

Listed:
  • Katsuhiro Tanaka

    (Department of Industrial and Systems Engineering, Keio University, 3-14-1, Hiyoshi, Minatokita-ku, Yokohama-shi, Kanagawa 223-8522, Japan)

  • Rei Yamamoto

    (Department of Industrial and Systems Engineering, Keio University, 3-14-1, Hiyoshi, Minatokita-ku, Yokohama-shi, Kanagawa 223-8522, Japan)

Abstract

This paper proposes two improvements to the support vector machine (SVM): (i) extension to a semi-positive definite quadratic surface, which improves the discrimination accuracy; (ii) addition of a variable selection constraint. However, this model is formulated as a mixed-integer semi-definite programming (MISDP) problem, and it cannot be solved easily. Therefore, we propose a heuristic algorithm for solving the MISDP problem efficiently and show its effectiveness by using corporate credit rating data.

Suggested Citation

  • Katsuhiro Tanaka & Rei Yamamoto, 2022. "Identification of best discrimination surface by mixed-integer semi-definite programming for support vector machine," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(04), pages 1-16, December.
    • Handle: RePEc:wsi:ijfexx:v:09:y:2022:i:04:n:s2424786321500420
    DOI: 10.1142/S2424786321500420
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    Cited by:

    1. Katsuhiro Tanaka & Rei Yamamoto, 2023. "Ellipsoidal buffered area under the curve maximization model with variable selection in credit risk estimation," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.

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