Credit Risk Evaluation with Least Square Support Vector Machine

2006 ◽  
pp. 490-495 ◽  
Author(s):  
Kin Keung Lai ◽  
Lean Yu ◽  
Ligang Zhou ◽  
Shouyang Wang
Keyword(s):  
Support Vector Machine ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Least Square ◽  
Support Vector

Support vector machine based multiagent ensemble learning for credit risk evaluation

2010 ◽  
Vol 37 (2) ◽  
pp. 1351-1360 ◽  
Author(s):  
Lean Yu ◽  
Wuyi Yue ◽  
Shouyang Wang ◽  
K.K. Lai
Keyword(s):  
Support Vector Machine ◽  
Credit Risk ◽  
Ensemble Learning ◽  
Risk Evaluation ◽  
Support Vector

scholarly journals Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lean Yu
Keyword(s):  
Support Vector Machine ◽  
Computational Complexity ◽  
Credit Risk ◽  
Least Squares ◽  
Risk Evaluation ◽  
Least Squares Method ◽  
Support Vector ◽  
Generalization Capability ◽  
Fuzzy Support Vector Machine ◽  
Vector Machines

A least squares fuzzy support vector machine (LS-FSVM) model that integrates advantages of fuzzy support vector machine (FSVM) and least squares method is proposed for credit risk evaluation. In the proposed LS-FSVM model, the purpose of incorporating the concepts of fuzzy sets is to add generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the computational complexity. For illustrative purposes, a real-world credit risk dataset is used to test the effectiveness and robustness of the proposed LS-FSVM methodology.

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