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Classification of companies using maximal margin ellipsoidal surfaces

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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.

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Acknowledgements

This research was supported in part by Grant-in-Aid for Scientific Research of the MEXT Grant Nos. (B) 21310096 and 19651070. Also, we are deeply grateful for Japan Credit Rating Agency, Ltd.

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  1. Department of Industrial and Systems Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Hiroshi Konno & Masato Saito

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  1. Hiroshi Konno
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  2. Masato Saito
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Correspondence to Masato Saito.

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Konno, H., Saito, M. Classification of companies using maximal margin ellipsoidal surfaces. Comput Optim Appl 55, 469–480 (2013). https://doi.org/10.1007/s10589-012-9508-5

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