Ji Yu Zhi Chi Xiang Liang Ji Fang Fa De Pu Tao Jiu Zhi Liang Yu Ce Yan Jiu Research on Wine Quality Prediction Based on Support Vector Machine Method
Sui Zhao Ren Men Sheng Huo Shui Ping Bu Duan De Ti Gao ,Pu Tao Jiu Yue Lai Yue Shou Dao Ren Men De Xi Ai Pu Tao Jiu De Chan Liang Yue Lai Yue Da Ran Er Pu Tao Jiu Zhi Liang Jian Ding Shou Duan Huan Shi Jin Kao Pin Jiu Shi De Ren Gong Pin Chang Da Fen Lai Pan Ding Pu Tao Jiu Zhi Liang De Hao Pi ,Xian Ran Zhe Chong Jian Ding Fang Shi Nan Yi Man Zu Dang Jin Shi Chang De Xu Qiu Xian Zai You Bu Shao Xue Zhe Yun Yong Shu Ju Wa Jue Zhong De Yi Xie Suan Fa (Bi Ru LogisticDuo Xiang Mo Xing ,Ren Gong Shen Jing Wang Luo ,Zhi Chi Xiang Liang Ji ,Jue Ce Shu ,Bagging,AdaBoost,Zui Jin Lin Fang Fa Deng Suan Fa )Lai Dui Pu Tao Jiu Zhi Liang Jin Xing Yu Ce Yan Jiu ,Qi Jie Guo Bing Bu Shi Hen Hao (Wu Pan Lu Jun Zai 15%Yi Shang ,Dan Xiang Dui Yu Jin Kao Pin Jiu Shi De Ren Gong Pin Chang Da Fen Lai Pan Ding ,Qi Jie Guo Huan Shi Jiao Wei Ke Kao De ,Tong Guo Qian Ren De Yan Jiu Ke Yi Zhi Dao Jin Jin Jian Dan Shi Yong Zhi Chi Xiang Liang Ji Zhong De Chang Jian He Han Shu ,Bing Bu Neng Hen Hao De Yu Ce Pu Tao Jiu Zhi Liang ,Yin Ci Ben Wen Ji Yu Zhi Chi Xiang Liang Ji Fang Fa De He Han Shu Jin Xing Xiu Gai ,Zhu Yao Jiang Zhi Chi Xiang Liang Ji Fang Fa Zhong Chang Jian He Han Shu Jin Xing Xian Xing Zu He Er De Dao Xin De He Han Shu Ben Wen Tong Guo Shi Yong UCIShu Ju Ku Zhong De "Wine Quality Data Set"De Shu Ju Lai Yan Zheng Ben Wen Suo Ti Chu De Fang Fa Yu Shu Ju Wa Jue Chang Yong De Suan Fa Jin Xing Dui Bi ,Tong Guo Shi Zhe Jiao Cha Yan Zheng De Fang Fa Lai Pan Duan Fang Fa De Hao Pi With the continuous improvement of people's living standards, wine has become more and more popular among people. Wine production is growing. However, the quality of the wine is still only determined by wine tasters' grading, which is obviously difficult to meet the needs of today's market. Many scholars use data mining algorithms (such as Logistic multinomial model, artificial neural network, support vector machine, decision tree, Bagging, AdaBoost, nearest neighbor algo rithm) to predict the wine quality. The results are not very good. The results are reliable and can be used to support vector machine (SVM) which can be used to predict the quality of wine. In this paper, we use the Quality Data Set UCI data to verify the proposed method and the data mining al gorithm for comparison, using ten fold cross validation method to determine the quality of the method.