The Application of Data Mining in Cigarette Sensory Quality Evaluation: an Experimental Study
作者单位：College of Information Science and Engineering of Northeastern University Technology Center of Hongta Tobacco Group Co.Ltd.
会议名称：《第26届中国控制与决策会议》
会议日期：2014年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)]
基 金：financially supported by the National Science Foundation of China(NSFC Proj.71171039,61273204 and 71021061) the Fundamental Research Funds for Central Universities(Proj.N110204005)
关 键 词：Sensory Quality Evaluation Classification Algorithms Data Mining Experimental Study
摘 要：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.