  The DNA microarray technology has modernized the approach of biology research
in such a way that scientists can now measure the expression levels of
thousands of genes simultaneously in a single experiment. Gene expression
profiles, which represent the state of a cell at a molecular level, have great
potential as a medical diagnosis tool. But compared to the number of genes
involved, available training data sets generally have a fairly small sample
size for classification. These training data limitations constitute a challenge
to certain classification methodologies. Feature selection techniques can be
used to extract the marker genes which influence the classification accuracy
effectively by eliminating the un wanted noisy and redundant genes This paper
presents a review of feature selection techniques that have been employed in
micro array data based cancer classification and also the predominant role of
SVM for cancer classification.
