A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification
The emergence of DNA Microarray technology has enabled researchers to analyze the
expression level of thousands of genes simultaneously. The Microarray data analysis is the process of nding
the most informative genes as well as remove redundant and irrelevant genes. One of the most important
applications of the Microarray data analysis is cancer classi cation. However, the curse of dimensionality and
the curse of sparsity make classifying gene expression pro les a challenging task. One of the most effective
methods to overcome these challenges is feature (gene) selection. In this paper, we aim to reviewand compare
the most recent hybrid approaches that employ bio-inspired evolutionary methods as the wrapper method.
The emergence of DNA Microarray technology has enabled researchers to analyze the
expression level of thousands of genes simultaneously. The Microarray data analysis is the process of …
Several hybrid gene selection algorithms for cancer classi cation that employ bio-inspired
evolutionary wrapper algorithm have been proposed in the literature and show good classi cation…