Soft Computing

, Volume 17, Issue 4, pp 643–650

A total least squares proximal support vector classifier for credit risk evaluation


DOI: 10.1007/s00500-012-0936-z

Cite this article as:
Yu, L. & Yao, X. Soft Comput (2013) 17: 643. doi:10.1007/s00500-012-0936-z


In this paper, a total least squares (TLS) version of proximal support vector machines (PSVM) is proposed for credit risk evaluation. The formulation of this new model is different from the original PSVM model, so a novel iterative algorithm is proposed to solve this model. A simulation test is first implemented on a classic two-spiral dataset, and then an empirical experiment is conducted on two publicly available credit datasets. The experimental results show that the proposed total least squares PSVM (TLS-PSVM) is at least comparable with PSVM and better than other models including standard SVM model.


Total least squares methodProximal support vector machineCredit risk evaluation

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.School of Economics and ManagementBeijing University of Chemical TechnologyBeijingChina
  2. 2.Alibaba Business CollegeHangzhou Normal UniversityHangzhouChina
  3. 3.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  4. 4.Business School University of EdinburghEdinburghUK