Efficient gene selection for classification of microarray data

Microarray is a useful technique for measuring expression data of thousands of genes simultaneously. One of challenges in classification of microarray data is to select a minimal number of relevant genes which can maximize classification accuracy. Many gene selection methods as well as their corresponding classifiers have been proposed. One of existing analysis methods is the hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD). In this paper, an intelligent genetic algorithm (IGA) using control genes and an improved fitness function is proposed to determine the minimal number of relevant genes and identify these genes, while maximizing classification accuracy simultaneously. The experimental results show that our approach is superior to the existing method GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy, especially for the datasets which have numerous categories and a large number of testing genes inside.