Redundant Gene Selection Based on Particle Swarm Optimization

Redundant gene selection is an important topic in the field of bioinformatics. This paper proposes a novel algorithm on Redundant Gene Selection by Particle Swarm Optimization (RGS-PSO), which tries to find a compact gene subset with great predictive ability. Compared with the previous works, RGS-PSO measures the redundancy of feature set by the maximum feature inter-correlation, which is more reasonable than those by the averaged inter-correlation. The outstanding performance of RGS-PSO has been examined by the experiments on several real world microarray data sets.