A Greedy Correlation-Incorporated SVM-Based Algorithm for Gene Selection

Microarrays serve scientists as a powerful and efficient tool to observe thousands of genes and analyze their activeness in normal or cancerous tissues. In general microarrays are used to measure the expression levels of thousands of genes in a cell mixture. Gene expression data obtained from microarrays can be used for various applications. One such application is that of gene selection. Gene selection is very similar to the feature selection problem addressed in the machine learning area. In a nutshell gene selection is the problem of identifying a minimum set of genes that are responsible for certain events (for example the presence of cancer). Informative gene selection is an important problem arising in the analysis of microarray data. In this paper, we present a novel algorithm for gene selection that combines support vector machines with gene correlations. Experiments show that the new algorithm, called GCI-SVM, obtains a higher classification accuracy using a smaller number of selected genes than the well-known algorithms in the literature.