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The invention of 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. Although this technology has shifted a new era in molecular classification, interpreting microarray data still remain a challenging issue due to their innate nature of “high dimensional low sample size”. Therefore, robust and accurate feature selection methods are required to identify differentially expressed genes across varied samples for example between cancerous and normal cells. Successful of feature selection techniques will assist to correctly classify different cancer types and consequently led to a better understanding of genetic signatures in cancers and would improve treatment strategies. This paper presents a review of feature selection techniques that have been employed in microarray data analysis. Moreover, other problems associated with microarray data analysis also addressed. In addition, several trends were noted including highly reliance on filter techniques compared to wrapper and embedded, a growing direction towards ensemble feature selection techniques and future extension to apply feature selection in combination of heterogeneous data sources.