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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)
Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines
Hong Kong, China
December 18-December 22
ISBN: 0-7695-2702-7
ASCII Text
x 
 
Lean Yu, Kin Keung Lai, Shouyang Wang, "Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines," Data Mining Workshops, International Conference on, pp. 823-827, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICDMW.2006.54,
author = {Lean Yu and Kin Keung Lai and Shouyang Wang},
title = {Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines},
journal ={Data Mining Workshops, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2702-7},
pages = {823-827},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.54},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining Workshops, International Conference on
TI - Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines
SN - 0-7695-2702-7
SP823
EP827
A1 - Lean Yu,
A1 - Kin Keung Lai,
A1 - Shouyang Wang,
PY - 2006
KW - null
VL - 0
JA - Data Mining Workshops, International Conference on
ER -
 
Lean Yu, Chinese Academy of Sciences,Beijing, 100080, China
Kin Keung Lai, Hong Kong; College of Business Administration, Hunan University, China
Shouyang Wang, Chinese Academy of Sciences, Beijing, 100080, China
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 (LSFSVM) 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.
Citation:
Lean Yu, Kin Keung Lai, Shouyang Wang, "Credit Risk Assessment with Least Squares Fuzzy Support Vector Machines," icdmw, pp.823-827, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
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