Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier
Author
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yu, Lean. 2014. Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier. Discrete Dynamics in Nature and Society،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-481112
Modern Language Association (MLA)
Yu, Lean. Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier. Discrete Dynamics in Nature and Society No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-481112
American Medical Association (AMA)
Yu, Lean. Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier. Discrete Dynamics in Nature and Society. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-481112
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-481112