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

Katsuhiro Tanaka () and Rei Yamamoto
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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

International Journal of Financial Engineering (IJFE), 2022, vol. 09, issue 04, 1-16

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.

Keywords: Mixed-integer semi-definite programming; cutting-plane algorithm; discriminant surface; support vector machine; variable selection (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S2424786321500420

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