Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier
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 generalizat...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2014/564213 |
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| _version_ | 1832556050862047232 |
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| author | Lean Yu |
| author_facet | Lean Yu |
| author_sort | Lean Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2e73ee27aa0849898a97a80b38347e02 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-2e73ee27aa0849898a97a80b38347e022025-02-03T05:46:23ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/564213564213Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines ClassifierLean Yu0Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, ChinaA 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.http://dx.doi.org/10.1155/2014/564213 |
| spellingShingle | Lean Yu Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier Discrete Dynamics in Nature and Society |
| title | Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier |
| title_full | Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier |
| title_fullStr | Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier |
| title_full_unstemmed | Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier |
| title_short | Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier |
| title_sort | credit risk evaluation with a least squares fuzzy support vector machines classifier |
| url | http://dx.doi.org/10.1155/2014/564213 |
| work_keys_str_mv | AT leanyu creditriskevaluationwithaleastsquaresfuzzysupportvectormachinesclassifier |