A Multi-instance Multi-label Learning Approach to Objective Auscultation Analysis of Traditional Chinese Medicine
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 S vowels i.e. Is/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 patients 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.
Auscultation traditional Chinese medicine multi-instance multi-label learning
Jianjun Yan Qingwei Shen Jintao Ren Yiqin Wang Chunfeng Chen Rui Guo Haixia Yan
Center for Mechatronics Engineering East China University of Science and Technology Shanghai 200237, TCM Syndrome Lab Shanghai University of TCM Shanghai 201203, China
国际会议
上海
英文
1638-1642
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）