Credit risk assessment with least squares fuzzy support vector machines

Lean Yu; Kin Keung Lai; Shouyang Wang

doi:10.1109/icdmw.2006.54

Credit risk assessment with least squares fuzzy support vector machines

Lean Yu, Kin Keung Lai, Shouyang Wang

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

7 Citations (Scopus)

Abstract

In this study, we discuss a least squares version of fuzzy support vector machine (FSVM) classifiers for designing a credit risk assessment system to discriminate good creditors from bad ones. Relative to the classical FSVM, the least squares FSVM (LS-FSVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a real-world credit dataset is used to test the effectiveness of the LS-FSVM. © 2006 IEEE.

Original language	English
Title of host publication	Proceedings - ICDM Workshops 2006
Subtitle of host publication	Sixth IEEE International Conference on Data Mining - Workshops
Editors	Shusaku Tsumoto, Christopher W. Clifton, Ning Zhong
Publisher	IEEE
Pages	823-827
ISBN (Electronic)	9780769527024
ISBN (Print)	0-7695-2792-2
DOIs	https://doi.org/10.1109/icdmw.2006.54
Publication status	Published - Dec 2006
Event	6th IEEE International Conference on Data Mining - Workshops (ICDM 2006) - Hong Kong, China Duration: 18 Dec 2006 → 18 Dec 2006

Publication series

Name
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	6th IEEE International Conference on Data Mining - Workshops (ICDM 2006)
Place	China
City	Hong Kong
Period	18/12/06 → 18/12/06

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10.1109/icdmw.2006.54

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title = "Credit risk assessment with least squares fuzzy support vector machines",

abstract = "In this study, we discuss a least squares version of fuzzy support vector machine (FSVM) classifiers for designing a credit risk assessment system to discriminate good creditors from bad ones. Relative to the classical FSVM, the least squares FSVM (LS-FSVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a real-world credit dataset is used to test the effectiveness of the LS-FSVM. {\textcopyright} 2006 IEEE.",

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

year = "2006",

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doi = "10.1109/icdmw.2006.54",

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address = "United States",

note = "6th IEEE International Conference on Data Mining - Workshops (ICDM 2006) ; Conference date: 18-12-2006 Through 18-12-2006",

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Yu, L, Lai, KK & Wang, S 2006, Credit risk assessment with least squares fuzzy support vector machines. in S Tsumoto, CW Clifton & N Zhong (eds), Proceedings - ICDM Workshops 2006: Sixth IEEE International Conference on Data Mining - Workshops. IEEE, pp. 823-827, 6th IEEE International Conference on Data Mining - Workshops (ICDM 2006), Hong Kong, China, 18/12/06. https://doi.org/10.1109/icdmw.2006.54

Credit risk assessment with least squares fuzzy support vector machines. / Yu, Lean; Lai, Kin Keung; Wang, Shouyang.
Proceedings - ICDM Workshops 2006: Sixth IEEE International Conference on Data Mining - Workshops. ed. / Shusaku Tsumoto; Christopher W. Clifton; Ning Zhong. IEEE, 2006. p. 823-827.

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

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AB - In this study, we discuss a least squares version of fuzzy support vector machine (FSVM) classifiers for designing a credit risk assessment system to discriminate good creditors from bad ones. Relative to the classical FSVM, the least squares FSVM (LS-FSVM) can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a real-world credit dataset is used to test the effectiveness of the LS-FSVM. © 2006 IEEE.

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T2 - 6th IEEE International Conference on Data Mining - Workshops (ICDM 2006)

Y2 - 18 December 2006 through 18 December 2006

ER -

Credit risk assessment with least squares fuzzy support vector machines

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