文献类型：期刊文献

英文题名：A multi-instance multi-label learning approach to objective auscultation analysis of traditional Chinese medicine

作者：Yan, Jianjun[1]; Shen, Qingwei[1]; Ren, Jintao[1]; Wang, Yiqin[2]; Chen, Chunfeng[2]; Guo, Rui[2]; Yan, Haixia[2]

机构：[1] Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] TCM Syndrome Lab, Shanghai University, TCM, Shanghai 201203, China

年份：2011

卷号：3

起止页码：1626

外文期刊名：Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

收录：EI(收录号：20120314690694)

语种：英文

外文关键词：Biomedical engineering - Learning systems - Medicine

摘要：The purpose of this paper is to study objective auscultation of traditional Chinese medicine using multi-instance multi-label (MIML) learning. The experiment data are the patients' speech samples of 5 vowels i.e. /a/,/o/,/e/,/i/,/u/. Each patient in the dataset may have one or both of the qi and yin deficiency syndromes. By regarding the 5 vowel samples from one patient as instances and the patient's syndrome type as the labels, the problem can be properly formalized under multi-instance multi-learning framework. In the conducted experiment, features are extracted from the speech samples and processed by MIML algorithm for classification. Satisfactory performance is obtained which proves that MIML is an effective and feasible approach for auscultation analysis. ? 2011 IEEE.