JACIII Vol.21 No.6 pp. 998-1008
doi: 10.20965/jaciii.2017.p0998


Wine Evaluation Modeling Based on Lasso and Support Vector Regression

Yanyun Yao*,**, Bing Xu*, and Jinghui He**

*Research Institute of Economic Statistics and Quantitative Economics, Zhejiang Gongshang University
18 Xuezheng Road, Xiasha University Town, Hangzhou 310018, China

**Department of Mathematics, Shaoxing University
900 Chengnan Avenue, Yuecheng District, Shaoxing 312000, China

December 25, 2016
April 20, 2017
October 20, 2017
Lasso, support vector regression, wine evaluation, reliability test, nonlinear effect

Wine consumption is gaining popularity, and significant attention has been given to its quality. In the present paper, an objective evaluation model along with a reliability test via Lasso and nonlinear effect test via support vector regression (SVR) is proposed. The digital simulation is finished with the experimental data obtained from the A problem of CUMCM-2012 (China Undergraduate Mathematical Contest in Modeling in 2012). The results of Lasso regression show that the wine quality mainly depends upon eight physicochemical indicators. Further research results of SVR imply that with several training samples, a good evaluation can be realized, denoting that our model based on Lasso SVR can significantly reduce the costs of measurement and appraisal. Compared to other relevant articles, this paper builds an objective and credible wine evaluation system where the physicochemical indicators and the latent nonlinear effect are considered. Moreover, the evaluation costs are taken into account.

Cite this article as:
Y. Yao, B. Xu, and J. He, “Wine Evaluation Modeling Based on Lasso and Support Vector Regression,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.6, pp. 998-1008, 2017.
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Last updated on Jul. 19, 2018