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

Lean Yu, Xiao Yao

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)643-650
JournalSoft Computing
Volume17
Issue number4
DOIs
Publication statusPublished - Apr 2013

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