Abstract：
BACKGROUND:Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Principle component analysis (PCA) and partial least squares (PLS) are two frequently used feature extraction methods, and in the previous works, the top several components of PCA or PLS are selected for modeling according to the descending order of eigenvalues. While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods. RESULTS:We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data. Here we have considered both an unsupervised method PCA and a supervised method PLS for extracting new components, genetic algorithms for feature selection, and support vector machines and k nearest neighbor for classification. Experimental results illustrate that our proposed framework is effective to select feature subsets and to reduce classification error rates. CONCLUSION:Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.
journal_name
BMC Genomicsjournal_title
BMC genomicsauthors
Li GZ,Bu HL,Yang MQ,Zeng XQ,Yang JYdoi
10.1186/1471-2164-9-S2-S24subject
Has Abstractpub_date
2008-09-16 00:00:00pages
S24issn
1471-2164pii
1471-2164-9-S2-S24journal_volume
9 Suppl 2pub_type
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