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Classification of companies using maximal margin ellipsoidal surfaces

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  • Hiroshi Konno
  • Masato Saito

Abstract

We recently proposed a data mining approach for classifying companies into several groups using ellipsoidal surfaces. This problem can be formulated as a semi-definite programming problem, which can be solved within a practical amount of computation time by using a state-of-the-art semi-definite programming software. It turned out that this method performs better for this application than earlier methods based on linear and general quadratic surfaces. In this paper we will improve the performance of ellipsoidal separation by incorporating the idea of maximal margin hyperplane developed in the field of support vector machine. It will be demonstrated that the new method can very well simulate the rating of a leading rating company of Japan by using up to 18 financial attributes of 363 companies. This paper is expected to provide another evidence of the importance of ellipsoidal separation approach in credit risk analysis. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Hiroshi Konno & Masato Saito, 2013. "Classification of companies using maximal margin ellipsoidal surfaces," Computational Optimization and Applications, Springer, vol. 55(2), pages 469-480, June.
    • Handle: RePEc:spr:coopap:v:55:y:2013:i:2:p:469-480
    DOI: 10.1007/s10589-012-9508-5
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    References listed on IDEAS

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    1. Galindo, J & Tamayo, P, 2000. "Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications," Computational Economics, Springer;Society for Computational Economics, vol. 15(1-2), pages 107-143, April.
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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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