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

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

Zhuoxi Yu, Huansen Zhang, Zhiwen Zhao, Limin Wang
Copyright: © 2016 |Volume: 14 |Issue: 2 |Pages: 13
ISSN: 1539-2937|EISSN: 1539-2929|EISBN13: 9781466688957|DOI: 10.4018/JECO.2016040106
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MLA

Yu, Zhuoxi, et al. "Prediction of E-Commerce Credit Rating based on PCA-SVR." JECO vol.14, no.2 2016: pp.74-86. http://doi.org/10.4018/JECO.2016040106

APA

Yu, Z., Zhang, H., Zhao, Z., & Wang, L. (2016). Prediction of E-Commerce Credit Rating based on PCA-SVR. Journal of Electronic Commerce in Organizations (JECO), 14(2), 74-86. http://doi.org/10.4018/JECO.2016040106

Chicago

Yu, Zhuoxi, et al. "Prediction of E-Commerce Credit Rating based on PCA-SVR," Journal of Electronic Commerce in Organizations (JECO) 14, no.2: 74-86. http://doi.org/10.4018/JECO.2016040106

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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.

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