Feature selection and classification in gene expression cancer data

Gene Expression data comprising more number genes and samples are used for cancer classification. Gene Expression information provides the gene expression level contributing to specific action. Cancer classification is done using the gene expression data. In our work, feature selection methods such as mutual information and genetic algorithm are used for examining cancer microarray Gene expression Data. By using feature selection methods most probable cancer associated genes are selected from large microarray Gene Expression data. The objective of the present work to attain enhanced classification accuracy. We have used the colon cancer dataset contains 2000 gene has 62 types, in which 40 are colon cancer tissue's types and the other 20 are normal types.