A multi-instance multi-label learning approach to objective auscultation analysis of traditional Chinese medicine
A multi-instance multi-label learning approach to objective auscultation analysis of traditional Chinese medicine
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DOI：
10.1109/bmei.2011.6098544
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Jianjun Yan;Qingwei Shen;Jintao Ren;Yiqin Wang;Chunfeng Chen;Rui Guo;Haixia Yan
中科院分区：
文献类型：
--
作者：
Jianjun Yan;Qingwei Shen;Jintao Ren;Yiqin Wang;Chunfeng Chen;Rui Guo;Haixia Yan
本文的目的是研究中医客观听诊的多示例多标记（MIML）学习。实验数据为患者/a/、/o/、/e/、/i/、/u/五个元音的语音样本。数据集中的每个患者可能具有气虚证和阴虚证之一或两者。以同一患者的5个元音样本为实例，以患者的证候类型为标签，在多实例多学习框架下将问题形式化。在实验中，从语音样本中提取特征，并通过MIML算法进行分类处理。实验结果表明，MIML是一种有效可行的听诊分析方法。
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.