Classification of companies using maximal margin ellipsoidal surfaces

Authors:
Hiroshi Konno;Masato Saito
Affiliations:
Department of Industrial and Systems Engineering, Chuo University, Tokyo, Japan 112-8551;Department of Industrial and Systems Engineering, Chuo University, Tokyo, Japan 112-8551
Venue:
Computational Optimization and Applications
Year:
2013

Citing 3
Cited 0

Support-Vector Networks

Machine Learning
Credit Risk Assessment Using Statistical and MachineLearning: Basic Methodology and Risk Modeling Applications

Computational Economics - Computational Studies at Stanford
Choosing the best set of variables in regression analysis using integer programming

Journal of Global Optimization

Quantified Score

Hi-index	0.00

Visualization

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.