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Title of Journal: Comput Optim Appl

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Abbravation: Computational Optimization and Applications

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Springer US

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1573-2894

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Classification of companies using maximal margin e

Authors: Hiroshi Konno Masato Saito
Publish Date: 2013/04/24
Volume: 55, Issue: 2, Pages: 469-480
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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 semidefinite programming problem which can be solved within a practical amount of computation time by using a stateoftheart semidefinite programming software It turned out that this method performs better for this application than earlier methods based on linear and general quadratic surfacesIn 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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Other Papers In This Journal:
  1. A benchmark library and a comparison of heuristic methods for the linear ordering problem
  2. Compression and denoising using l 0 -norm
  3. AAR-based decomposition algorithm for non-linear convex optimisation
  4. Multi-step nonlinear conjugate gradient methods for unconstrained minimization
  5. Optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria
  6. Minimization of the Tikhonov functional in Banach spaces smooth and convex of power type by steepest descent in the dual
  7. Numerical experiments with an inexact Jacobian trust-region algorithm
  8. An interior-point approach for primal block-angular problems
  9. Computing the metric dimension of graphs by genetic algorithms
  10. Some non-interior path-following methods based on a scaled central path for linear complementarity problems
  11. On the resolution of certain discrete univariate max–min problems
  12. A coordinate gradient descent method for ℓ 1 -regularized convex minimization
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