An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
An accelerated procedure for recursive feature ranking on microarray data
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Supervised locally linear embedding for plant leaf image feature extraction
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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Gene expression data that are gathered from tissue samples are expected to significantly help the development of efficient tumor diagnosis and classification platforms. Since DNA microarray experiments provide us with huge amount of gene expression data and only a few of genes are related to tumor, gene selection algorithms should be emphatically explored to extract those informative genes related tumor from gene expression data. So we propose a novel feature selection approach to further improve the SVM-based classification performance of gene expression data, which projects high dimensional data onto lower dimensional feature space. We examine a set of gene expression data that include sets of tumor and normal clinical samples by means of SVMs classifier. Experiments show that SVM has a superior performance in classification of gene expression data as long as the selected features can represent the principal components of all gene expression samples.