A Classification Method Based on ManifoldLearning for Gene Microarray Data
[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
33卷
Number of periods:
2012 05
Page number:
121-124
Column:
Public date:
2012-09-10
- Title:
- A Classification Method Based on ManifoldLearning for Gene Microarray Data
- Keywords:
- manifold learning; classification; gene; mieroarray data
- CLC:
- TP181
- DOI:
- 10.3969/j.issn.1671-6833.2012.05.027
- 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.
- References:
- -
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Last Update:
1900-01-01