Abstrait

Classification of Microarray Data Based On Feature Selection Method

C.Lavanya, M.Nandihini, R.Niranjana, C.Gunavathi

Genes are encoding regions that form necessary building block inside the cell and show the way to proteins which are achieving a variety of functions. However, some genes may get mutated. Such genes are responsible for cancer occurrence. It can be discovered by closely examining samples taken from patients to identify faulty genes. Gene expression dataset usually comes with only dozens of tissues/samples but with thousands or even tens of thousands of genes/features. In this paper, we employ feature selection techniques for analyzing cancer microarray gene expression data. Feature selection technique is used to select the most possibly cancerrelated genes from huge microarray gene expression data. It aims to achieve improved classification performance. This can be achieved by the measures of T-Test, Chi-Square Test and Information gain. Cancer classification using microarray data poses another major challenge because of the huge number of genes compared to the number of tissue samples. Only a small number of genes in the microarray data which consisting of thousands of genes show strong correlation with the target phenotypes. This paper presents the Naive Bayes algorithm for the classification task. A comprehensive framework that incorporates feature selection and classification techniques is capable of successfully classifying new samples as infected or normal