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The DNA microarray technology has modernized the approach of biology researchin such a way that scientists can now measure the expression levels ofthousands of genes simultaneously in a single experiment. Gene expressionprofiles, which represent the state of a cell at a molecular level, have greatpotential as a medical diagnosis tool. But compared to the number of genesinvolved, available training data sets generally have a fairly small samplesize for classification. These training data limitations constitute a challengeto certain classification methodologies. Feature selection techniques can beused to extract the marker genes which influence the classification accuracyeffectively by eliminating the un wanted noisy and redundant genes This paperpresents a review of feature selection techniques that have been employed inmicro array data based cancer classification and also the predominant role ofSVM for cancer classification.

Review on Feature Selection Techniques and the Impact of SVM for Cancer Classification using Gene Expression Profile