Optimization and Classification Techniques in Microarray Medical Data for Gene Selection: A Survey

Abstract

        The aim of the paper is to analysis the various techniques involve in the classification of microarray medical data. Major researches concentrated on the new gene extraction technique that is known as gene selection, which has a higher impact on the classification result. By selecting the related genes of the disease from the enormous genes, helps to classify the disease more accurately. Some optimization techniques applied for the selection of gene and one of the well-known method is modified Particle Swarm Optimization (PSO) and that method is explained in the paper. Some researchers used different types of classifier to test the performance of the method.  The minimum redundancy and maximum relevance features along with Support Vector Machine (SVM) and achieved the accuracy up to 98% for 8 genes.

 Keywords: Microarray medical data, Minimum redundancy and maximum relevance features Particle Swarm Optimization, Support Vector Machine.