Dilated Cardiomyopathy Metabolomics Data Classification Based on DAE-SVM Algorithm
Dilated Cardiomyopathy Metabolomics Data Classification Based on DAE-SVM Algorithm
复制标题
基于DAE-SVM算法的扩张型心肌病代谢组学数据分类
作者：
Mingyang Jiang;Yanchun Liang;Zhili Pei;Xiye Wang;Jingqing Jiang;Qinghu Wang;Renchu Guan
提出了一种结合支持向量机的深度自动编码网络(DAE-SVM)，用于代谢组学数据的整理。传统的特征提取和分类由于样本量小、参数高维、非线性、含噪声等特点，很难达到令人满意的效果。DAE使用隐藏层执行非线性转换，从而了解复杂的关系。它具有坚实的高阶表现能力，能够提取更复杂的代谢组学数据特征。本文采用Boltzmann机器完成DAE的预训练，采用共轭梯度法完成微调，支持向量机完成分类。在阐述的心肌病实际代谢组学数据上的实证结果表明，与现有的其他算法相比，该模型取得了最好的效果。
A novel deep auto-encoder network combined with support vector machine (DAE-SVM) are prepared for the arrangement of metabolomics data. Because of their small sample size, high-dimensional, nonlinear and noisy parameters, traditional feature abstraction and classifications are very difficult to achieve satisfactory results. DAE performs non-linear transformations with hidden layers, which can learn complex relationships. It has a solid capability to show high-order appearance and can excerpt more complicated characteristics of metabolomic data. This manuscript considers Boltzmann Machine to complete the pre-training of DAE, the conjugate gradient was adopted to complete the fine-tuning, and SVM completes the classification. Empirical results on actual metabolomics data of expound cardiomyopathy concluded the proposed model has attained best accomplishment compared to other extant algorithms.
登录
查看更多内容
影响因子：
24.5
作者：
Wu GD;Compher C;Chen EZ;Smith SA;Shah RD;Bittinger K;Chehoud C;Albenberg LG;Nessel L;Gilroy E;Star J;Weljie AM;Flint HJ;Metz DC;Bennett MJ;Li H;Bushman FD;Lewis JD
通讯作者：
Lewis JD
DOI：
--
发表时间：
2011-12
期刊：
--
影响因子：
--
作者：
R. Socher;E. Huang;Jeffrey Pennington;A. Ng;Christopher D. Manning
通讯作者：
R. Socher;E. Huang;Jeffrey Pennington;A. Ng;Christopher D. Manning
影响因子：
3.9
作者：
Yudong Zhang;Zhang Yang;Huimin Lu-;Xingxing Zhou;Preetha Phillips;Qing-Ming Liu;Shuihua Wang
通讯作者：
Yudong Zhang;Zhang Yang;Huimin Lu-;Xingxing Zhou;Preetha Phillips;Qing-Ming Liu;Shuihua Wang
DOI：
10.1109/ijcnn.2010.5596468
发表时间：
2010-07
期刊：
The 2010 International Joint Conference on Neural Networks (IJCNN)
影响因子：
--
作者：
S. Lange;Martin A. Riedmiller
通讯作者：
S. Lange;Martin A. Riedmiller
DOI：
10.1016/j.patcog.2016.06.013
发表时间：
2016-12
期刊：
Pattern Recognit.
影响因子：
--
作者：
Wing W. Y. Ng;Guangjun Zeng;Jianjun Zhang;D. Yeung;W. Pedrycz
通讯作者：
Wing W. Y. Ng;Guangjun Zeng;Jianjun Zhang;D. Yeung;W. Pedrycz