Classification of companies using maximal margin ellipsoidal surfaces

作     者：Konno, Hiroshi Saito, Masato 

作者机构：Chuo Univ Dept Ind & Syst Engn Bunkyo Ku Tokyo 1128551 Japan 

出 版 物：《COMPUTATIONAL OPTIMIZATION AND APPLICATIONS》 (计算优化及其应用)

年 卷 期：2013年第55卷第2期

页      面：469-480页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：MEXT [21310096, 19651070] Grants-in-Aid for Scientific Research [21310096, 19651070] Funding Source: KAKEN 

主　　题：Credit risk Classification of companies Rating Ellipsoidal surface Maximal margin hyperplane Semi-definite programming 

摘      要：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.