| 多标记中医问诊数据的症状选择 |
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| 引用本文： | 邵欢,李国正,刘国萍,王忆勤.多标记中医问诊数据的症状选择[J].中国科学：信息科学,2011(11). |
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| 作者姓名： | 邵欢 李国正 刘国萍 王忆勤 |
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| 作者单位： | 上海大学计算机工程与科学学院;同济大学控制科学与工程系教育部服务计算与嵌入式系统重点实验室;上海中医药大学中医四诊信息化综合实验室; |
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| 基金项目： | 国家自然科学基金(批准号:60873129,30901897,61005006); 上海市重点学科(批准号:S30302,B004); 模式识别国家重点实验室开放课题资助项目 |
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| 摘 要： | 中医诊断中,一个患者可能兼有多个证型标记,其计算机辅助诊断是高维数据多标记学习的一个典型应用.中医问诊过程中往往会产生大量症状,这影响诊断算法建模的效果.特征选择旨在寻求最小的相关症状特征子集,且能使模型泛化能力达到最大.目前有关多标记数据特征选择的研究还很少,本文提出使用一种组合的优化技术进行中医问诊多标记数据的症状选择,通过多标记k近邻等4个算法进行建模.本文所提算法与当前流行的多种多标记数据降维算法如MEFS(多标记嵌入式特征选择方法)、MDDM(多标记特征降维方法)进行了比较,在UCI酵母多标记数据集和一个冠心病问诊数据上的实验结果显示本文算法较之已有多种算法有明显提高,在average precision上对分类器的提高可达10.62%和14.54%.论文实现了冠心病问诊症候模型的建立,为冠心病的诊断和其他多标记数据分析提供了有效的参考.
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| 关 键 词： | 多标记学习 特征选择 高维 中医问诊 冠心病 |
Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine |
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| SHAO Huan,LI GuoZheng,LIU GuoPing & WANG YiQin School of Computer Engineering , Science,Shanghai University,Shanghai ,China.Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine[J].Scientia Sinica Informationis,2011(11). |
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| Authors: | SHAO Huan LI GuoZheng LIU GuoPing & WANG YiQin School of Computer Engineering Science Shanghai University Shanghai China |
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| Affiliation: | SHAO Huan1,LI GuoZheng2,LIU GuoPing3 & WANG YiQin3 1 School of Computer Engineering and Science,Shanghai University,Shanghai 200072,China,2 Department of Control Science and Engineering,Key Laboratory of Ministry of Education for Service Computing and Embedded Systems,Tongji University,Shanghai 201804,3 Laboratory of Information Access and Synthesis of TCM Four Diagnosis,Shanghai University of Traditional Chinese Medicine,Shanghai 201203 |
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| Abstract: | In traditional Chinese medicine(TCM)diagnosis,a patient may be associated with more than one syndrome tags,and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional data.It is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis,which affects the modeling of diagnostic algorithm.Feature selection entails choosing the smallest feature subset of relevant symptoms,and maximizing the generalization performance of the mo... |
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| Keywords: | multi-label learning feature selection high-dimensionality inquiry of traditional Chinese medicine coronary heart disease |
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