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Soft Computing

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01.04.2013 | Focus

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

verfasst von: Lean Yu, Xiao Yao

Erschienen in: Soft Computing | Ausgabe 4/2013

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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.

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Titel: A total least squares proximal support vector classifier for credit risk evaluation
verfasst von: Lean Yu
Xiao Yao
Publikationsdatum: 01.04.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 4/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0936-z

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