An efficient gene selection technique based on Self-organizing Map and Particle Swarm Optimization

Among the large amount of genes presented in microarray gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. It is for this reason that reducing the dimensionality of gene expression data is imperative. An improved Self-organizing map method based on neighborhood mutual information correlation measure is proposed, and then combines with Particle swarm optimization method to construct an efficient gene selection algorithm, denoted by ICMSOM-PSO. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.