Classification of companies using maximal margin ellipsoidal surfaces

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 pro...

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Bibliographic Details
Published in:Computational optimization and applications 2013-06, Vol.55 (2), p.469-480
Main Authors: Konno, Hiroshi, Saito, Masato
Format: Article
Language:English
Subjects:
Analysis
Classification
Computation
Computer programming
Computer programs
Convex and Discrete Geometry
Credit risk
Data mining
Discriminant analysis
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization
Programming
Ratings
Risk analysis
Risk assessment
Separation
Simulation
Software
Statistics
Studies
Support vector machines
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Summary: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.
ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-012-9508-5