[1]李强,石陆魁,刘恩海,等.基于流形学习的基因微阵列数据分类方法[J].郑州大学学报(工学版),2012,33(05):121-124.[doi:10.3969/j.issn.1671-6833.2012.05.027]
　LI Qiang,SHI Lukui,LIU Enhai,et al.A Classification Method Based on ManifoldLearning for Gene Microarray Data[J].Journal of Zhengzhou University (Engineering Science),2012,33(05):121-124.[doi:10.3969/j.issn.1671-6833.2012.05.027]
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基于流形学习的基因微阵列数据分类方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
33
期数:
2012年05期
页码:
121-124
栏目:
出版日期:
2012-09-10

文章信息/Info

Title:
A Classification Method Based on ManifoldLearning for Gene Microarray Data
作者:
李强; 石陆魁; 刘恩海; 等.
河北工业大学计算机科学与软件学院,天津,300401, 河北工业大学计算机科学与软件学院,天津,300401, 河北工业大学计算机科学与软件学院,天津,300401, 河北工业大学计算机科学与软件学院,天津,300401
Author(s):
LI Qiang; SHI Lukui; LIU Enhai; etc;
School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401, China
关键词:
流形学习;  分类;  基因;  微阵列数据
Keywords:
manifold learning;  classification;  gene;  mieroarray data
分类号:
TP181
DOI:
10.3969/j.issn.1671-6833.2012.05.027
摘要:
提出了一种结合流形学习方法与分类算法的基因微阵列数据分类模型,先用流形学习算法对基因微阵列数据进行降维处理,然后再对降维后的数据进行分类.在实验中将流形学习算法LLE、ISOMAP、LE和LTSA与三种分类算法相结合,并与直接用高维数据进行分类的结果进行了比较,实验结果表明所提出的模型极大地提高了分类精度,同时也提高了分类算法的执行效率.
Abstract:
Each sample in gene microarray data contains thousands or even tens of thousands of genes. lt isnecessary to reduce the dimension of the data before classifying them for obtaining better classified results.Manifold learning, as a nonlinear dimension reduction method, can discover the intrinsic laws hidden in thehigh dimensional data and has been widely applied in areas such as pattern recognition. A model combiningmanifold learning with classified algorithms was proposed to classify microarray data. In the model, the dimen.sion of microarray data was firstly reduced with some manifold learning method. Then the data reduced the di.mension were classified, In experiments, several manifold learing algorithms ineluding LLE, ISOMAP, LEand L’TSA are combined with three classified methods. And the results are compared with those from directlyclassifying high dimensional data. Experiments showed that the classification accuracy was great improved withthe proposed model. Moreover, the execute elficieney of classification algorithms was also greatly inereased.

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更新日期/Last Update: 1900-01-01