The Application of Data Mining in Cigarette Sensory Quality Evaluation:an Experimental Study
To study the effectiveness of classification algorithms in cigarette sensory quality evaluation,chemical components such as total sugar,protein,potassium,etc.are taken as condition attributes,and ID3,C4.5,rough set,BP neural network,support vector machine,and k-nearest-neighbor are adopted to predict cigarette sensory quality index,such as luster,aroma,harmony,offensive odor,irritation and aftertaste.The experimental results show that harmony reaches the best classification accuracy with about 95%,and the effectiveness of luster and offensive odor are slightly below the harmony with 85%-90%by SVM and KNN,while aroma has the worst result.In addition,offensive odor and aftertaste are fairly accurate with about 70%.As a whole,SVM and KNN have the better performance in the prediction of cigarette sensory quality than the other classification algorithms.
Sensory Quality Evaluation Classification Algorithms Data Mining Experimental Study
ZHANG Zhongliang TANG Jianguo LUO Xinggang TANG Jiafu MENG Zhaoyu QIAO Danna
College of Information Science and Engineering of Northeastern University,Shenyang 110819,China Technology Center of Hongta Tobacco Group Co.,Ltd.,Yuxi 653100,Yunnan,China
国际会议
长沙
英文
1328-1332
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）