Asarum Subspecies Identification with Pattern Recognition Techniques

摘要：

Chinese medicine pharmacologists examine the chemical features of Chinese medicine materials for identification of the subspecies. In this paper, three different types of chemical data, namely main constituent content, inorganic element content and HPLC fingerprint data of 54 asarum samples are tested and analyzed. Some types of data with strong connection with the sample subspecies classification are firstly filtered out with Principle Component Analysis and separability measure. Chemical features of these data types are then ranked with concern of the correlation with the sample subspecies using SV.M RFE. At last, the effect of the filtered out chemical features on the sample subspecies classification are verified using leave-one-out strategy.

关键词： Subspecies Identification Principle Component Analysis Separability SVM RFE

作者: Shiwen Zhang Yixu Song Yannan Zhao Peifa Jia Mingying Shang Shaoqing Cai

作者单位: Dept. of Computer Science and Technology Tsinghua University Beijing, China School of Pharmaceutical Sciences Peking University Beijing, China

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Data Storage and Data Engineering(DSDE 2011)(2011年第二届数据存储与数据工程国际会议)

会议地点: 西安

会议语种:英文

页码: 34-38

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）