Title :
Asarum subspecies identification with pattern recognition techniques
Author :
Zhang, Shiwen ; Song, Yixu ; Zhao, Yannan ; Jia, Peifa ; Shang, Mingying ; Cai, Shaoqing
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
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 SVM RFE. At last, the effect of the filtered out chemical features on the sample subspecies classification are verified using leave-one-out strategy.
Keywords :
medicine; microorganisms; pattern recognition; pharmaceutical technology; principal component analysis; support vector machines; Asarum subspecies identification; Chinese medicine materials; Chinese medicine pharmacologists; HPLC fingerprint data; SVM RFE; chemical data; chemical features; inorganic element content; leave-one-out strategy; main constituent content; pattern recognition techniques; principle component analysis; sample subspecies classification; separability measure; Fingerprint recognition; Materials; Principle Component Analysis; SVM RFE; Separability; Subspecies Identification;
Conference_Titel :
Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-61284-485-5
DOI :
10.1109/ICCSN.2011.6014382
