Analysis of SAGE Results with Combined Learning Techniques

Serial analysis of gene expression (SAGE) experiments could provide us the expression level of thousands of genes in a biological sample. However, the number of available samples is relatively small. Such undersampled problem needs to be carefully analyzed from a machine learning perspective. In this paper, we combine several state-of-theart techniques for classification, feature selection, and error estimation, and evaluate the performance of the combined techniques on the SAGE dataset. Our results show that a novel algorithm, support vector machine with the stump kernel, performs well on the SAGE dataset both for building an accurate classifier, and for selecting relevant features.