A least squares bilateral-weighted fuzzy SVM method to evaluate credit risk

Wei Huang; Kin Keung Lai; Lean Yu; Shouyang Wang

doi:10.1109/ICNC.2008.660

A least squares bilateral-weighted fuzzy SVM method to evaluate credit risk

Wei Huang, Kin Keung Lai, Lean Yu, Shouyang Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this study, we propose a least squares bilateral-weighted fuzzy support vector machine(LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability by treating each training sample as being both a possible good and bad customer and considering bilateral-weighted classification errors. For illustration purpose, a real-world credit risk assessment dataset is used to test the effectiveness of the LS-BFSVM method. © 2008 IEEE.

Original language	English
Title of host publication	Proceedings - 4th International Conference on Natural Computation, ICNC 2008
Pages	13-17
Volume	7
DOIs	https://doi.org/10.1109/ICNC.2008.660
Publication status	Published - 2008
Event	4th International Conference on Natural Computation, ICNC 2008 - Jinan, China Duration: 18 Oct 2008 → 20 Oct 2008

Publication series

Name
Volume	7

Conference

Conference	4th International Conference on Natural Computation, ICNC 2008
Place	China
City	Jinan
Period	18/10/08 → 20/10/08

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title = "A least squares bilateral-weighted fuzzy SVM method to evaluate credit risk",

abstract = "In this study, we propose a least squares bilateral-weighted fuzzy support vector machine(LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability by treating each training sample as being both a possible good and bad customer and considering bilateral-weighted classification errors. For illustration purpose, a real-world credit risk assessment dataset is used to test the effectiveness of the LS-BFSVM method. {\textcopyright} 2008 IEEE.",

author = "Wei Huang and Lai, {Kin Keung} and Lean Yu and Shouyang Wang",

year = "2008",

doi = "10.1109/ICNC.2008.660",

language = "English",

isbn = "9780769533049",

volume = "7",

pages = "13--17",

booktitle = "Proceedings - 4th International Conference on Natural Computation, ICNC 2008",

note = "4th International Conference on Natural Computation, ICNC 2008 ; Conference date: 18-10-2008 Through 20-10-2008",

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A least squares bilateral-weighted fuzzy SVM method to evaluate credit risk. / Huang, Wei; Lai, Kin Keung; Yu, Lean et al.
Proceedings - 4th International Conference on Natural Computation, ICNC 2008. Vol. 7 2008. p. 13-17 4667936.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

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AU - Lai, Kin Keung

AU - Yu, Lean

AU - Wang, Shouyang

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N2 - In this study, we propose a least squares bilateral-weighted fuzzy support vector machine(LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability by treating each training sample as being both a possible good and bad customer and considering bilateral-weighted classification errors. For illustration purpose, a real-world credit risk assessment dataset is used to test the effectiveness of the LS-BFSVM method. © 2008 IEEE.

AB - In this study, we propose a least squares bilateral-weighted fuzzy support vector machine(LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization ability by treating each training sample as being both a possible good and bad customer and considering bilateral-weighted classification errors. For illustration purpose, a real-world credit risk assessment dataset is used to test the effectiveness of the LS-BFSVM method. © 2008 IEEE.

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DO - 10.1109/ICNC.2008.660

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780769533049

VL - 7

SP - 13

EP - 17

BT - Proceedings - 4th International Conference on Natural Computation, ICNC 2008

T2 - 4th International Conference on Natural Computation, ICNC 2008

Y2 - 18 October 2008 through 20 October 2008

ER -

A least squares bilateral-weighted fuzzy SVM method to evaluate credit risk

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