Feature Selection with Fluid Mechanics Inspired Particle Swarm Optimization for Microarray Data

Deoxyribonucleic acid (DNA) microarray gene expression data has been widely utilized in the field of functional genomics, since it is helpful to study cancer, cells, tissues, organisms etc. But the sample sizes are relatively small compared to the number of genes, so feature selection is very necessary to reduce complexity and increase the classification accuracy of samples. In this paper, a completely new improvement over particle swarm optimization (PSO) based on fluid mechanics is proposed for the feature selection. This new improvement simulates the spontaneous process of the air from high pressure to low pressure, therefore it allows for a search through all possible solution spaces and prevents particles from getting trapped in a local optimum. The experiment shows that, this new improved algorithm had an elaborate feature simplification which achieved a very precise and significant accuracy in the classification of 8 among the 11 datasets, and it is much better in comparison with other methods for feature selection.