Credit Risk Evaluation Using: Least Squares Support Vector Machine with Mixture of Kernel

2016 ◽  
Author(s):  
Liwei Wei ◽  
Wenwu Li ◽  
Qiang Xiao
Keyword(s):  
Support Vector Machine ◽  
Credit Risk ◽  
Least Squares ◽  
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.

A MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER WITH APPLICATION TO CREDIT RISK ANALYSIS

2009 ◽  
Vol 08 (04) ◽  
pp. 697-710 ◽  
Author(s):  
LEAN YU ◽  
SHOUYANG WANG ◽  
JIE CAO
Keyword(s):  
Support Vector Machine ◽  
Risk Analysis ◽  
Credit Risk ◽  
Least Squares ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Original Form ◽  
Simple Modification ◽  
Credit Risk Analysis

In this paper, a modified least squares support vector machine classifier, called the C-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importance. The C-VLSSVM classifier can be obtained by a simple modification of the regularization parameter, based on the least squares support vector machine (LSSVM) classifier, whereby more weight is given to errors in classification of important classes, than to errors in classification of unimportant classes, while keeping the regularized terms in their original form. For illustration purpose, two real-world credit data sets are used to verify the effectiveness of the C-VLSSVM classifier. Experimental results obtained reveal that the proposed C-VLSSVM classifier can produce promising classification results in credit risk analysis, relative to other classifiers listed in this study.

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