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Feature selection from microarray data has become an ever evolving area of research. Numerous techniques have widely been applied for extraction of genes which are expressed differentially in microarray data. Some of these comprise of studies related to fold-change approach, classical t-statistics and modified t-statistics. It has been found that the gene lists returned by these methods are dissimilar...
A micro array represents thousands of gene expression levels across a few samples. Determination of an optimal set of features from such a high dimensional dataset requires a good feature selection method. Based on statistical significance of the features, an elimination of insignificant genes can be performed. However such methods lack biological validation. In this paper we propose a method where...
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