High dimensionality of microarray data and expressions of thousands of features in a much
smaller number of samples is a challenge affecting the applicability of the analytical results.
However Support Vector Machine (SVM) has been commonly used in the classification of
microarray datasets, yet the problem of high dimensionality of the feature space of data still
exist. This study deals with the reduction of gene expression data into a minimal subset of
genes, by introducing feature selection, to greatly reduce computational burden and noise …