Gene Selection Method Based on Neighborhood Rough Sets and Fish Swarm Intelligence

Abstract

Facing the gene expression data with high dimension, small samples and uncertainty, a gene selection method based on neighborhood rough sets and fish swarm intelligence is proposed by fusing a fuzzy tolerance granulation technology and a fish swarm intelligence algorithm with global optimization ability. Firstly, the neighborhood rough sets are used to granulate the gene data and form some neighborhood particles. Secondly, the neighborhood classification accuracy is presented as an uncertainty evaluation function that aims to judge these neighborhood particles and distinguish key genes. Furthermore, a gene selection algorithm based on artificial fish swarm intelligence is designed. Finally, some gene selection experiments are carried out on two tumor gene data sets. The classification experiments of a small number of selected key genes are conducted by using SVM classifier. The experimental results show that the genes selected by our proposed method have a low redundancy and a better classification performance.