Affiliations: [a] College of Computer and Information Engineering, Henan Normal University, Xinxiang, P.R. China | [b] Engineering and Technology Research Center for Computational Intelligence and Data Mining of Universities of Henan Province, Xinxiang, P.R. China
Correspondence:
[*]
Corresponding author. Jiu-Cheng Xu, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P.R. China. E-mail: xjch3701@sina.com.
Abstract: 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.
