Title :
The Graphical Feature Extraction of Star Plot for Wine Quality Classification
Author :
Li Jing ; Wang Jin-Jia ; Zhang Tao ; Hong, Wen-xue ; Hong Wen-Xue
Author_Institution :
Coll. of Sci.; Coll. of Electr. Eng., Yanshan Univ., Qinhuangdao, China
Abstract :
We propose a visualization method of evaluation of wine quality. The wine data are from the certification phase of the physicochemical analysis test. The data include the 11 input variables, an output variable which is the quality of wine. The data include 1599 samples of red wine and 4898 samples of white wine. Our method works better than the traditional neural networks and support vector machine method, and has visual advantages. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.
Keywords :
beverages; certification; data visualisation; feature extraction; pattern classification; production engineering computing; quality control; certification phase; graphical feature extraction; oenologist wine tasting; physicochemical analysis test; star plot; target marketing; visualization method; wine production; wine quality classification; Artificial neural networks; Classification algorithms; Feature extraction; Gallium; Pattern recognition; Support vector machines; Visualization; feature extraction; graphical representation of the multivariate data; support vector machines; visual evaluation;
Conference_Titel :
Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-8043-2
Electronic_ISBN :
978-0-7695-4180-8
DOI :
10.1109/PCSPA.2010.192