A subset selection method using Filter and wrapper algorithms based on the nature of the expression values in microarray data sets for gene feature selection

Abstract

In the field of micro-array data analysis the crucial first step is gene selection. The process refers to selecting a subset consisting of a few genes which are of genetic significance out of thousands of genes to make the job of the classifier algorithm computationally easy and efficient at the same time. Feature selection plays an important role in classification. The first set of data are gene expression profiles from Acute Lymphoblastic Leukemia (ALL) patients. In this paper an algorithm is proposed for feature subset selection (FFS) which is based on the nature of intensity values in microarray datasets. The proposed method is a combination of a filter and a wrapper algorithm which selects subsets. It is based on two assumptions. Our results demonstrate the importance of feature selection in accurately classifying new samples