Prediction of E-Commerce Credit Rating based on PCA-SVR

Zhuoxi Yu, Huansen Zhang, Zhiwen Zhao and Limin Wang
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Zhuoxi Yu: Faculty of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China
Huansen Zhang: Faculty of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China
Zhiwen Zhao: College of Mathematics, Jilin Normal University, Siping, China
Limin Wang: Faculty of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China

Journal of Electronic Commerce in Organizations (JECO), 2016, vol. 14, issue 2, 74-86

Abstract: Using the principal component analysis (PCA) and support vector regression machine (SVR) in predicting the credit rating of online stores. Collects 14 variables, including 982 observations of dress shops in Taobao. Firstly, the authors use the method of PCA to filter and reduce the dimension of the data, and obtain five factors, namely, the evaluation factor, the traffic factor, the price factor, the preferential policies factor and the reliability factor. Secondly, they use the PCA's output as the input of the SVR for the credit rating prediction. Thirdly, they extract 300 as the training samples and 150 as the test samples from the data, and utilize GA algorithm for parameter optimization in order to improve the prediction accuracy of SVR. Finally, carry on an empirical test. The result shows that this combination method is accurate and effective in prediction rate than the consequences of the traditional SVR, and it is valid and feasible.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jeco00:v:14:y:2016:i:2:p:74-86

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