DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO

Abstract

DNA microarray technology is a novel method to monitor expression levels of large number of genes simultaneously. These gene expressions can be and is being used to detect various forms of diseases. Using multiple microarray datasets, this paper cross compares two different methods for classification and feature selection. Since individual gene count in microarray datas are too many, most informative genes should be selected and used. For this selection, we have tried Relief and LASSO feature selection methods. After selecting informative genes from microarray data, classification is performed with Support Vector Machines (SVM) and Multilayer Perceptron Networks (MLP) which both are widely used in multiple classification tasks. The overall accuracy with LASSO and SVM outperforms most of the approaches proposed.