A Kernel Principal Component Analysis Based Least Squares Fuzzy Support Vector Machine Methodology with Variable Penalty Factors for Credit Classification
YU Lean, WANG Shouyang
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Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190; Research Center for Financial Engineering and Financial Management, Changsha 410114
Credit classification is one of the most important tasks in credit risk management and its main purpose is to provide credit decisions for credit granting institutions using the classification results. In order to correctly classify the different credit applicants, especially for default applicants, a kernel principal component analysis based least squares fuzzy support vector machine methodology with variable penalty factors integrating kernel principal component analysis and least squares fuzzy support vector machine with variable penalty factors is proposed for credit classification. In the proposed methodology, the sample data is first preprocessed, and then the data dimension is reduced by kernel principal component analysis. Subsequently, the reduced data is used for classification analysis using least squares fuzzy support vector machine with variable penalty factors. For verification purpose, two publicly credit datasets are used for empirical analysis. Experimental results revealed that the proposed kernel principal component analysis based least squares fuzzy support vector machine methodology with variable penalty factors can obtain better classification results than other approaches listed in this study.
YU Lean
, WANG Shouyang. , {{custom_author.name_en}}.
A Kernel Principal Component Analysis Based Least Squares Fuzzy Support Vector Machine Methodology with Variable Penalty Factors for Credit Classification. Journal of Systems Science and Mathematical Sciences, 2009, 29(10): 1311-1326 https://doi.org/10.12341/jssms08462
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